import sys
import time
import datetime
import os
import random
import numpy as np
import pandas as pd
from scipy import stats
from bokeh.plotting import figure, output_file, save
from cycler import cycler
%matplotlib notebook
import matplotlib.pyplot as plt
def readFile(ID, GR, testMode = False):
try:
if testMode:
start = time.perf_counter()
path = os.path.join(os.getcwd(),'MergedData/')
fileName = str(ID) + "-" + str(GR) + ".xlsx"
pathToData = os.path.join(path, fileName)
data = pd.read_excel(pathToData, index_col=0, sheet_name = None)
if testMode:
print(f"readFile in {time.perf_counter() - start}s for participant {ID} group {GR}")
return data
except Exception as e:
print("error in readFile")
print(e)
def readAllData(participantsDict, testMode = False):
try:
if testMode:
start = time.perf_counter()
listOfDataFrames = []
for key, value in participantsDict.items():
listOfDataFrames.append(readFile(key, value))
if testMode:# or fullTestMode:
print(f"\nreadAllData in {time.perf_counter() - start}s")
return listOfDataFrames
except Exception as e:
print("error in readAllData")
print(e)
def getIndexForPhase(experimentDataList, testMode = False):
try:
if testMode or fullTestMode:
start = time.perf_counter()
breakLenghts = []
for dataDictionary in experimentDataList:
firstInd = True
secInd = False
thirdInd = False
fourthInd = False
fifthInd = False
adaptationPEphase = ()
instructionPhase = ()
breakPhase = ()
adaptationVEphase = ()
phaseStart = 0
phaseEnd = 0
i = 0
for item in dataDictionary['Experiment_Data'].itertuples():
if firstInd and item[3] == 'adaptation PE':
firstInd = False
phaseStart = i
secInd = True
elif secInd and item[3] != 'adaptation PE' and item[18] == 'Audio_1' :
secInd = False
adaptationPEphase = (phaseStart, i-1)
phaseStart = i
thirdInd = True
elif thirdInd and item[18] != 'Audio_1':
#2ms difference in break start and audioClip playing caused an error here for sample 30
thirdInd = False
instructionPhase = (phaseStart, i-1)
phaseStart = i
fourthInd = True
elif fourthInd and item[3] != 'break':
fourthInd = False
breakPhase = (phaseStart, i - 1)
phaseStart = i
fifthInd = True
elif fifthInd and item[3] != 'adaptation VE':
adaptationVEphase = (phaseStart, i - 1)
break
i += 1
dataDictionary['phases'] = adaptationPEphase, instructionPhase, breakPhase, adaptationVEphase
breakLenghts.append(breakPhase)
if testMode:
print(f"adaptationPEphase : {adaptationPEphase}")
print(f"instructionPhase : {instructionPhase}")
print(f"breakPhase : {breakPhase}")
print(f"adaptationVEphase : {adaptationVEphase}")
print('_' * 100 + '1')
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[0]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[0]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[0]+1])
print("to")
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[1]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[1]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationPEphase[1]+1])
print('_' * 100+ '2')
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[0]-1])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[0]])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[0]+1])
print("to")
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[1]-1])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[1]])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[instructionPhase[1]+1])
print('_' * 100+ '3')
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[0]-1])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[0]])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[0]+1])
print("to")
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[1]-1])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[1]])
print(dataDictionary['Experiment_Data']['Audio_clip'].iloc[breakPhase[1]+1])
print('_' * 100+ '4')
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[0]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[0]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[0]+1])
print('to')
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[1]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[1]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[breakPhase[1]+1])
print('_' * 100+ '5')
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[0]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[0]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[0]+1])
print('to')
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[1]-1])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[1]])
print(dataDictionary['Experiment_Data']['Phase'].iloc[adaptationVEphase[1]+1])
print('_' * 100)
if testMode:
print('_'*100)
print(breakLenghts)
print('_'*100)
breakLenghts = [tup[1] - tup[0] for tup in breakLenghts]
breakLenghts.sort()
if testMode:
print(breakLenghts)
print('_'*100)
print(breakLenghts[0])
print('_'*100)
print(breakLenghts[-1])
print('_'*100)
converterToSeconds = lambda ms : divmod(ms,100)
breakPhaseConverted = []
for ms in breakLenghts:
s = converterToSeconds(ms)
breakPhaseConverted.append(f"{s[0]}''.{s[1]}")
breakLenghts = [item/100 for item in breakLenghts]
print('_'*100)
print(f'Shortest break : {breakLenghts[0]}')
print(f'Longest break : {breakLenghts[-1]}')
print('_'*80)
print(f'Mean break length : {np.mean(breakLenghts)}')
print(f'SD break length : {np.std(breakLenghts)}')
print('_'*40)
print(f'Median break length : {np.median(breakLenghts)}')
print(f'Q1 and Q3 break : {np.quantile(breakLenghts,[.25,.75])}')
fig6, ax6 = plt.subplots(figsize=(8, 8))
ax6.boxplot(breakLenghts, notch=True, vert = True, patch_artist = True, labels = ['Lenght of break stage'])
ax6.yaxis.grid(True)
ax6.set_ylabel('seconds')
ax6.set_title('Lenght of break stage')
ax6.set_prop_cycle(color=['red'])
fig6.show()
fig7, ax7 = plt.subplots(figsize=(8, 8))
ax7.hist(breakLenghts,30)
ax7.axvline(x=np.mean(breakLenghts), c = 'r')
ax7.axvline(x=np.mean(breakLenghts) + np.std(breakLenghts), c = 'r', ls = '--')
ax7.axvline(x=np.mean(breakLenghts) - np.std(breakLenghts), c = 'r', ls = '--')
ax7.set_ylabel('samples')
ax7.set_xlabel('seconds')
ax7.set_title('Lenght of break stage')
fig7.show()
if testMode or fullTestMode:
print(f"\ngetIndexForPhase in {time.perf_counter() - start}s")
return experimentDataList
except Exception as e:
print("error in getIndexForPhase")
print(e)
def calculateMeanAge(experimentDataList, testMode = False):
try:
if testMode or fullTestMode:
start = time.perf_counter()
malesAge = []
femalesAge = []
for item in experimentDataList:
if item['Participant_Data'][0].get('Gender') == 0:
malesAge.append(int(item['Participant_Data'][0].get('Age')))
else:
femalesAge.append(int(item['Participant_Data'][0].get('Age')))
ageList = [*malesAge, *femalesAge]
sampleAgeData = (np.mean(ageList), np.std(ageList), np.median(ageList), len(ageList))
print(f"Average participant age : {sampleAgeData[0]}")
print(f"Standard deviation of participant age : {sampleAgeData[1]}")
print(f"Median of participant age : {sampleAgeData[2]}")
print(f'N = {sampleAgeData[3]}')
sampleFemaleAgeData = (np.mean(femalesAge), np.std(femalesAge), np.median(femalesAge), len(femalesAge))
print('_'*80)
print(f'Mean female age : {sampleFemaleAgeData[0]}')
print(f"Standard deviation of female age : {sampleFemaleAgeData[1]}")
print(f"Median of female age : {sampleFemaleAgeData[2]}")
print(f'N = {sampleFemaleAgeData[3]}')
sampleMaleAgeData = (np.mean(malesAge), np.std(malesAge), np.median(malesAge), len(malesAge))
print(f'\nMean male age : {sampleMaleAgeData[0]}')
print(f"Standard deviation of male age : {sampleMaleAgeData[1]}")
print(f"Median of male age : {sampleMaleAgeData[2]}")
print(f'N = {sampleMaleAgeData[3]}')
l1 = [i for i in range(1, len(femalesAge)+1)]
l2 = ['Mean','Standard Deviation', 'Median', 'N']
l3 = ['-' for i in range(1, len(femalesAge)+1)]
dfIndex = [*l1,* l2]
ageDF = pd.DataFrame(columns = ['Females Age', 'Males Age', 'Average'], index = dfIndex)
ageDF['Females age'] = [*femalesAge, *list(sampleFemaleAgeData)]
ageDF['Males age'] = [*malesAge, *list(sampleMaleAgeData)]
ageDF['Average age'] = [*l3, *list(sampleAgeData)]
# on one plot:
allAgeData = [femalesAge, ageList, malesAge]
labels = ['Female age', 'Average age', 'Male age']
colors = ['lightcoral', 'forestgreen', 'lightskyblue'] # https://matplotlib.org/stable/gallery/color/named_colors.html
#plt.close()
fig1, ax1 = plt.subplots(figsize=(8, 8))
ax1.boxplot(allAgeData, notch=True, vert = True, patch_artist = True, labels = labels)
ax1.yaxis.grid(True)
ax1.set_ylabel('age')
ax1.set_title('Participants age')
#fig1.gca().set_color_cycle(['red', 'green', 'blue']) # deprecated
#cy = cycler('color', ['red', 'green', 'blue'])
#ax1.set_prop_cycle(cy)# ['lightcoral', 'forestgreen', 'lightskyblue'])
ax1.set_prop_cycle(color=['red', 'green', 'blue'])
"""
for ax, color in zip(ax1.gca(), colors):
ax.set_facecolor(color)
"""
fig1.show()
# to add violins:
# https://matplotlib.org/stable/gallery/statistics/customized_violin.html#sphx-glr-gallery-statistics-customized-violin-py
if testMode or fullTestMode:
print(f"\ncalculateMeanAge in {time.perf_counter() - start}s")
return ageDF
except Exception as e:
print('error in calculateMeanAge')
print(e)
def testingHRQualityPerSample(experimentDataList, showGraphs = False, testMode = False):
"""
Checking number of -1 in sample 'PPG_to_HR_CONSENSYS'
Total and phase wise
"""
try:
if testMode or fullTestMode:
start = time.perf_counter()
totalNa1all = []
totalNa2all = []
totalNa3all = []
totalNa4all = []
totalNaTotalAll = []
total1all = []
total2all = []
total3all = []
total4all = []
totalTotalAll = []
for item in experimentDataList:
totalNa1 = 0
totalNa2 = 0
totalNa3 = 0
totalNa4 = 0
totalNaAll = 0
total1 = 0
total2 = 0
total3 = 0
total4 = 0
totalAll = 0
if testMode:
ID = int(item['Participant_Data'][0].get('ID'))
if ID > 9:
ID = f'00{ID}'
elif ID > 99:
ID = 'f0{ID}'
elif ID > 999:
ID = 'f{ID}'
else:
ID = f'000{ID}'
print(ID)
totalNaInSample = 0
totalNaInAdaptationPEphase = 0
totalNaInInstructionPhase = 0
totalNaInBreakPhase = 0
totalNaInAdaptationVEphase = 0
start1 = item['phases'][0][0]
end1 = item['phases'][0][1]
start2 = item['phases'][1][0]
end2 = item['phases'][1][1]
start3 = item['phases'][2][0]
end3 = item['phases'][2][1]
start4 = item['phases'][3][0]
end4 = item['phases'][3][1]
i = 0
for val in item['Experiment_Data']['PPG_to_HR_CONSENSYS']:
if val == -1:
totalNaInSample += 1
if i < end1:
totalNaInAdaptationPEphase += 1
elif i < end2: #sometimes audio phase was overlaping
totalNaInInstructionPhase += 1
elif i < end3:
totalNaInBreakPhase += 1
elif i < end4:
totalNaInAdaptationVEphase += 1
i += 1
totalRows = len(item['Experiment_Data']['PPG_to_HR_CONSENSYS'])
if testMode:
print(f"\nstart1 : {start1} | end1 : {end1}")
print(f"start2 : {start2} | end2 : {end2}")
print(f"start3 : {start3} | end3 : {end3}")
print(f"start4 : {start4} | end4 : {end4}")
totalRowsInAdaptationPEphase = end1 - start1 +1
totalRowsInInstructionPhase = end2 - start2 +1
totalRowsInBreakPhase = end3 - start3 +1
totalRowsInAdaptationVEphase = end4 - start4 +1
if testMode:
print(f"\ntotalRows : {totalRows}")
print(f"totalRowsInAdaptationPEphase : {totalRowsInAdaptationPEphase}")
print(f"totalRowsInInstructionPhase : {totalRowsInInstructionPhase}")
print(f"totalRowsInBreakPhase : {totalRowsInBreakPhase}")
print(f"totalRowsInAdaptationVEphase : {totalRowsInAdaptationVEphase}")
toAppendInAdaptationPEphase = (totalNaInAdaptationPEphase,
totalRowsInAdaptationPEphase,
totalNaInAdaptationPEphase/totalRowsInAdaptationPEphase)
totalNa1all.append(totalNaInAdaptationPEphase)
total1all.append(totalRowsInAdaptationPEphase)
toAppendInInstructionPhase = (totalNaInInstructionPhase,
totalRowsInInstructionPhase,
totalNaInInstructionPhase/totalRowsInInstructionPhase)
totalNa2all.append(totalNaInInstructionPhase)
total2all.append(totalRowsInInstructionPhase)
toAppendInBreakPhase = (totalNaInBreakPhase,
totalRowsInBreakPhase,
totalNaInBreakPhase/totalRowsInBreakPhase)
totalNa3all.append(totalNaInBreakPhase)
total3all.append(totalRowsInBreakPhase)
toAppendInAdaptationVEphase = (totalNaInAdaptationVEphase,
totalRowsInAdaptationVEphase,
totalNaInAdaptationVEphase/totalRowsInAdaptationVEphase)
totalNa4all.append(totalNaInAdaptationVEphase)
total4all.append(totalRowsInAdaptationVEphase)
if testMode:
print(f'\ntoAppendInAdaptationPEphase : {toAppendInAdaptationPEphase}')
print(f'toAppendInInstructionPhase : {toAppendInInstructionPhase}')
print(f'toAppendInBreakPhase : {toAppendInBreakPhase}')
print(f'toAppendInAdaptationVEphase : {toAppendInAdaptationVEphase}')
toAppendTotal = (totalNaInSample,
totalRows,
totalNaInSample/totalRows)
totalNaTotalAll.append(totalNaInSample)
totalTotalAll.append(totalRows)
toAppend = {}
toAppend['adaptation PE phase'] = toAppendInAdaptationPEphase
toAppend['instruction phase'] = toAppendInInstructionPhase
toAppend['break phase'] = toAppendInBreakPhase
toAppend['adaptation VE phase'] = toAppendInAdaptationVEphase
toAppend['total'] = toAppendTotal
if testMode:
print(f"\ntoAppend for participant {ID} :\n {toAppend}")
print('_'*100)
item['Missing_HR'] = toAppend
totalNa1 += totalNaInAdaptationPEphase
totalNa2 += totalNaInInstructionPhase
totalNa3 += totalNaInBreakPhase
totalNa4 += totalNaInAdaptationVEphase
totalNaAll += totalNaInSample
total1 += totalRowsInAdaptationPEphase
total2 += totalRowsInInstructionPhase
total3 += totalRowsInBreakPhase
total4 += totalRowsInAdaptationVEphase
totalAll += totalRows
if testMode or fullTestMode:
totalErrors = (totalNa1/total1, totalNa2/total2, totalNa3/total3, total4 ,totalNa4/total4, totalNaAll/totalAll)
allResults = {}
allResults['total adaptation PE phase'] = (totalNa1, total1, totalErrors[0])
allResults['total instruction phase'] = (totalNa2, total2, totalErrors[1])
allResults['total break phase'] = (totalNa3, total3, totalErrors[2])
allResults['total adaptation VE phase'] = (totalNa4, totalErrors[3])
allResults['total mean'] = (totalNaAll, totalAll, totalErrors[4])
if testMode:
print('='*100)
print(allResults)
allPhaseErrorData = [[na / total for na, total in zip(totalNa1all, total1all)],
[na / total for na, total in zip(totalNa2all, total2all)],
[na / total for na, total in zip(totalNa3all, total3all)],
[na / total for na, total in zip(totalNa4all, total4all)],
[na / total for na, total in zip(totalNaTotalAll, totalTotalAll)]]
if testMode:
print('_'*200)
print('allPhaseErrorData')
print(allPhaseErrorData)
if showGraphs:
labels = ['Adaptation PE', 'Instruction', 'Break', 'Adaptation VE']
fig4, ax4 = plt.subplots(figsize=(8, 8))
ax4.boxplot(allPhaseErrorData[0:4], notch=True, vert = True, patch_artist = True, labels = labels)
ax4.yaxis.grid(True)
ax4.set_ylabel('Interpolated HR')
ax4.set_title('Per phase error distribution')
ax4.set_prop_cycle(color=['red', 'green', 'blue','yellow'])
fig4.show()
fig5, ax5 = plt.subplots(figsize=(8, 8))
ax5.boxplot(allPhaseErrorData[4], notch=True, vert = True, patch_artist = True, labels = ['Total error'])
ax5.yaxis.grid(True)
ax5.set_ylabel('Interpolated HR')
ax5.set_title('Total error distribution')
ax5.set_prop_cycle(color=['red'])
fig5.show()
if testMode or fullTestMode:
print(f"\ntestingHRQualityPerSample in {time.perf_counter() - start}s")
return experimentDataList
except Exception as e:
print("error in testingHRQualityPerSample")
print(e)
def calculateMeansPerPhase(experimentDataList, thresholds = [1], testMode = False):
try:
if testMode or fullTestMode:
start = time.perf_counter()
phases = ['adaptation PE', 'instruction', 'break', 'adaptation VE']
newNames = lambda item : f'threshold : {int((1-item) * 100)}%'
columnNames1 = [newNames(item) for item in thresholds]
columnNames3 = [f'targeted {newNames(item)}' for item in thresholds]
d = {}
for thresholdLevel in [*columnNames1, *columnNames3]:
d[thresholdLevel] = pd.DataFrame(columns = phases, index = pd.RangeIndex(0, len(experimentDataList)))
meansHR = pd.concat(d, axis=1)
columnNames2 = ['Skin Conductance', 'Phasic Component', 'Tonic Component']
d = {}
for parameter in columnNames2:
d[parameter] = pd.DataFrame(columns = phases, index = pd.RangeIndex(0, len(experimentDataList)))
meansSC = pd.concat(d, axis=1)
i = 0
for item in experimentDataList:
ID = int(item['Participant_Data'][0].get('ID'))
error1 = item['Missing_HR'].get('adaptation PE phase')[-1]
error2 = item['Missing_HR'].get('instruction phase')[-1]
error3 = item['Missing_HR'].get('break phase')[-1]
error4 = item['Missing_HR'].get('adaptation VE phase')[-1]
errorsDict = dict(zip(phases, [error1, error2, error3, error4]))
phaseLengths = []
rejected = False
j = 0
for phase in item['phases']:
sampleMeanPhaseHR = np.mean(item['Experiment_Data']['HR_CONSENSYS_interpolated'].iloc[phase[0]:phase[1]])
phaseLengths.append((phase[1] - phase[0])/100)
k = 0
for cutOff in thresholds:
#strict cutOff:
if error1 <= cutOff and error2 <= cutOff and error3 <= cutOff and error4 <= cutOff:
meansHR.loc[i,(columnNames1[k], phases[j])] = sampleMeanPhaseHR
elif testMode and not rejected:
rejected = True
print(f'{i}) At threshold : {1 - cutOff} sample {ID} was rejected from HR analysis')
#targeted cutOff:
if errorsDict[phases[j]] <= cutOff:
meansHR.loc[i,(columnNames3[k], phases[j])] = sampleMeanPhaseHR
elif testMode:
print(f'{i}) At threshold : {1 - cutOff} sample {ID} was rejected from targeted HR analysis in {phases[j]}')
k += 1
meansSC.loc[i,(columnNames2[0], phases[j])] = np.mean(item['Experiment_Data']['Skin_Conductance'].iloc[phase[0]:phase[1]])
meansSC.loc[i,(columnNames2[1], phases[j])] = np.mean(item['Experiment_Data']['Skin_Conductance_Phasic_CVX'].iloc[phase[0]:phase[1]])
meansSC.loc[i,(columnNames2[2], phases[j])] = np.mean(item['Experiment_Data']['Skin_Conductance_Tonic_CVX'].iloc[phase[0]:phase[1]])
j += 1
for l in range(len(phases)):
meansHR.loc[i,('Phases lengths', phases[l])] = meansSC.loc[i,('Phases lengths', phases[l])] = phaseLengths[l]
meansHR.loc[i,'Gender'] = meansSC.loc[i,'Gender'] = int(item['Participant_Data'][0].get('Gender'))
meansHR.loc[i,'Age'] = meansSC.loc[i,'Age'] = int(item['Participant_Data'][0].get('Age'))
meansHR.loc[i,'MSSQ'] = meansSC.loc[i,'MSSQ'] = int(item['Participant_Data'][0].get('MSSQ'))
meansHR.loc[i,'ID'] = meansSC.loc[i,'ID'] = ID
i += 1
if testMode or fullTestMode:
print('_'*100)
print('meansHR : \n')
display(meansHR)
print('_'*100)
print('\nmeansSC : \n')
display(meansSC)
print(f"\ncalculateMeansPerPhase in {time.perf_counter() - start}s")
return meansHR, meansSC
except Exception as e:
print('error in calculateMeansPerPhase')
print(e)
def statisticalTestsOnData(meansSC, meansHR, thresholds = [1], testMode = False):
try:
if testMode or fullTestMode:
start = time.perf_counter()
startHighlight0 = '\x1b[30;43m'
startHighlight1 = '\x1b[33;42m'
startHighlight2 = '\x1b[32;40m'
endHifghlight = '\x1b[0m'
phases = ['adaptation PE', 'instruction', 'break', 'adaptation VE']
parameters = ['Skin Conductance', 'Phasic Component', 'Tonic Component']
tests = []
phasesNo = len(phases)
for i in range(phasesNo):
if i == 0:
for j in range(1, phasesNo):
tests.append((phases[i], phases[j]))
elif i == phasesNo:
break
else:
for j in range(i+1, phasesNo):
tests.append((phases[i],phases[j]))
correls = ['Phases lengths','Age','MSSQ']# 'Gender' would had to be between groups as its 0 1 - further work
condition = lambda cond : f'{cond[0]} - {cond[1]}'
outerIndex = [condition(test) for test in tests]
newName = lambda item : f'threshold : {int((1-item) * 100)}%'
hrIndexes = [newName(threshold) for threshold in thresholds]
innerIndex = [*parameters, *hrIndexes]
columnNames = ['N',
'mean difference',
'standard deviation',
'Shapiro-Wilk statistic','Shapiro-Wilk pvalue',
'kurtosis statistic','kurtosis pvalue', 'skew',
'Ttest statistic', 'Ttest pvalue',
'Wilcoxon statistic', 'Wilcoxon pvalue']
d = {}
for outerInd in outerIndex:
d[outerInd] = pd.DataFrame(columns = columnNames, index = hrIndexes)
resultsDF = pd.concat(d, axis=0)
for test in tests:
outerI = condition(test)
for parameter in parameters:
df = pd.DataFrame({test[0] : meansSC.loc[:,(parameter, test[0])],
test[1] : meansSC.loc[:,(parameter, test[1])]})
df['delta'] = df[test[0]] - df[test[1]]
if testMode:
print('*-'*50)
print('df in statisticalTestsOnData :')
display(df)
print('-*'*40)
print('meansSC')
display(meansSC)
print('-*'*40)
print('meansHR')
display(meansHR)
print('*-'*50)
meanVal = np.mean(df['delta'])
stdVal = np.std(df['delta'])
shapiro = stats.shapiro(df['delta'])
kurtosis = stats.kurtosistest(df['delta'])
skew = stats.skew(df["delta"])
if testMode or fullTestMode:
print('\n' * 3 + startHighlight0 + f'_ ' * 12
+ f'Test of {parameter} for : {test[0]} - {test[1]}'
+ f' _' * 12 + endHifghlight)
print('_' * 100)
if testMode:
display(df)
display(df.dropna())
print('_' * 80 + '\n')
print(f'mean for differences in {parameter} for {test[0]} - {test[1]} : {meanVal}')
print(f'standard deviation for differences in {parameter} for {test[0]} - {test[1]} : {stdVal}')
print('_' * 80)
print('Shapiro-Wilk :')
print(shapiro)
print('_'*50)
print('kurtosistest :')
print(kurtosis)
print('_'*50)
print(f'Skew : {skew}')
print('_'*50)
fig2, ax2 = plt.subplots(figsize=(8, 8))
ax2.hist(df['delta'],20)
ax2.axvline(x=meanVal, c = 'r',
label = f'Mean {parameter} for {test[0]} - {test[1]} : {meanVal}')
ax2.axvline(x=meanVal + stdVal, c = 'r', ls = '--',
label = f'+SD : {meanVal + stdVal}')
ax2.axvline(x=meanVal - stdVal, c = 'r', ls = '--',
label = f'-SD : {meanVal - stdVal}')
ax2.legend()
fig2.show()
tTest = stats.ttest_rel(df[test[0]], df[test[1]])
wilcoxon = stats.wilcoxon(df[test[0]], df[test[1]])
print(startHighlight1 + '~' * 100 + endHifghlight)
print(startHighlight2 + f'>>> test results of {parameter} for {test[0]} - {test[1]} : ' + endHifghlight)
print(f'\nTtest results : {tTest}')
print('.'*100)
print(f'Wilcoxon signed-rank test results : {wilcoxon}\n')
print(startHighlight1 + '~' * 100 + endHifghlight)
results = [len(df[test[0]].dropna()),
meanVal, stdVal,
shapiro[0], shapiro[1],
kurtosis[0], kurtosis[1], skew,
tTest[0], tTest[1],
wilcoxon[0], wilcoxon[1]]
i = 0
for colName in columnNames:
resultsDF.loc[(outerI, parameter), colName] = results[i]
i += 1
#added for correlations: # 'Gender' would had to be between groups as its 0 1
for param in correls:
if param == 'Phases lengths':
df[param] = meansSC.loc[:,(param, 'break')]
title = 'Break length[s]'
else:
df[param] = meansSC.loc[:, param]
title = param
df.dropna(inplace = True)
print("_"*100)
print(f"correlation for {title} and {test[0]} - {test[1]} in {parameter} :")
pearsR = stats.pearsonr(df[param], df['delta'])
regressiveLineOrder1 = np.polynomial.polynomial.polyfit(df[param], df['delta'].astype(float), 1)
print(f'pearson r = {pearsR[0]} (p = {pearsR[1]})')
linearRegLineFunction = f' y = {regressiveLineOrder1[1]} * x + {regressiveLineOrder1[0]}'
print(f'regressive line first order : {linearRegLineFunction}')
fig8, ax8 = plt.subplots(figsize=(8, 8))
ax8.scatter(df[param], df['delta'])
ax8.plot(df[param], regressiveLineOrder1[1] * df[param] + regressiveLineOrder1[0],
'-', c = 'r', label = f'{linearRegLineFunction}')
ax8.set_title(f'{test[0]} - {test[1]} for {parameter} with {title} \n{pearsR}')
ax8.set_xlabel(f'{title}')
ax8.set_ylabel(f'{parameter} : {test[0]} - {test[1]}')
ax8.grid(True)
ax8.legend()
fig8.show()
for threshold in thresholds:
newName = lambda item : f'threshold : {int((1-threshold) * 100)}%'
parameter = newName(threshold)
if testMode:
print('=-'*50)
print('meansHR.loc[:,(parameter, test[0])]')
print(meansHR.loc[:,(parameter, test[0])])
df = pd.DataFrame({test[0] : meansHR.loc[:,(parameter, test[0])],
test[1] : meansHR.loc[:,(parameter, test[1])]})
df['delta'] = df[test[0]] - df[test[1]]
meanVal = np.mean(df['delta'])
stdVal = np.std(df['delta'])
shapiro = stats.shapiro(df['delta'].dropna())
kurtosis = stats.kurtosistest(df['delta'].dropna())
skew = stats.skew(df["delta"].dropna())
if testMode or fullTestMode:
print('\n' * 3 + startHighlight0 + f'_ ' * 12
+ f'Test of Heart Rate(HR)[{parameter}] for {test[0]} - {test[1]}'
+ f' _' * 12 + endHifghlight)
print('_' * 100)
if testMode:
display(df)
display(df.dropna())
print('_' * 80 + '\n')
print(f'mean for differences in HR[{parameter}] for {test[0]} - {test[1]} : {meanVal}')
print(f'standard deviation for differences in HR[{threshold}] for {test[0]} - {test[1]} : {stdVal}')
print('_' * 80)
print('Shapiro-Wilk :')
print(shapiro)
print('_'*50)
print('kurtosistest :')
print(kurtosis)
print('_'*50)
print(f'Skew : {skew}')
print('_'*50)
fig3, ax3 = plt.subplots(figsize=(8, 8))
ax3.hist(df['delta'].dropna(),20)
ax3.axvline(x=meanVal, c = 'r')
ax3.axvline(x=meanVal + stdVal, c = 'r', ls = '--')
ax3.axvline(x=meanVal - stdVal, c = 'r', ls = '--')
fig3.show()
tTest = stats.ttest_rel(df[test[0]].dropna(), df[test[1]].dropna())
wilcoxon = stats.wilcoxon(df[test[0]].dropna(), df[test[1]].dropna())
print(startHighlight1 + '~' * 100 + endHifghlight)
print(startHighlight2 + f'>>> test results of {parameter} for {test[0]} - {test[1]} : ' + endHifghlight)
print(f'\nTtest results : {tTest}')
print('.'*100)
print(f'Wilcoxon signed-rank test results : {wilcoxon}\n')
print(startHighlight1 + '~' * 100 + endHifghlight)
results = [len(df[test[0]].dropna()),
meanVal, stdVal,
shapiro[0], shapiro[1],
kurtosis[0], kurtosis[1], skew,
tTest[0], tTest[1],
wilcoxon[0], wilcoxon[1]]
i = 0
for colName in columnNames:
resultsDF.loc[(outerI, parameter), colName] = results[i]
i += 1
#added for correlations:
for param in correls:
if param == 'Phases lengths':
df[param] = meansSC.loc[:,(param, 'break')]
title = 'Break length[s]'
else:
df[param] = meansSC.loc[:,param]
title = param
df.dropna(inplace = True)
print("_"*100)
print(f"correlation for {title} and {test[0]} - {test[1]} in Heart Rate(HR)[{parameter}] :")
pearsR = stats.pearsonr(df[param], df['delta'])
regressiveLineOrder1 = np.polynomial.polynomial.polyfit(df[param], df['delta'].astype(float), 1)
print(f'pearson r = {pearsR[0]} (p = {pearsR[1]})')
linearRegLineFunction = f' y = {regressiveLineOrder1[1]} * x + {regressiveLineOrder1[0]}'
print(f'regressive line first order : {linearRegLineFunction}')
fig8, ax8 = plt.subplots(figsize=(8, 8))
ax8.scatter(df[param], df['delta'])
ax8.plot(df[param], regressiveLineOrder1[1] * df[param] + regressiveLineOrder1[0],
'-', c = 'r', label = f'{linearRegLineFunction}')
ax8.set_title(f'{test[0]} - {test[1]} for {parameter} with {title} \n{pearsR}')
ax8.set_xlabel(f'{title}')
ax8.set_ylabel(f'{parameter} : {test[0]} - {test[1]}')
ax8.grid(True)
ax8.legend()
fig8.show()
if testMode or fullTestMode:
print(f"\nstatisticalTestsOnData in {time.perf_counter() - start}s")
return resultsDF
except Exception as e:
print("error in statisticalTestsOnData")
print(e)
def saveResults(statisticalResults, meansSC, meansHR, ageDF, pathToSaveData = 'Results/'):
try:
filePath = pathToSaveData + 'ExperimentResults.xlsx'
with pd.ExcelWriter(os.path.join(os.getcwd(),filePath)) as writer:
statisticalResults.to_excel(writer, sheet_name='Statistical Results')
meansHR.to_excel(writer, sheet_name='Means Heart Rate')
meansSC.to_excel(writer, sheet_name='Means Skin Conductance')
ageDF.to_excel(writer, sheet_name='Means Age')
except Exception as e:
print("error in saveResults")
print(e)
def main(experimentDataList, testMode = False, devMode = False):
try:
if testMode or fullTestMode:
start = time.perf_counter()
randomSample = False
pd.options.display.max_rows = 100
pd.options.display.float_format = '{:.3f}'.format
thresholds = [1, .9, .5, .1]
AllParticipantsGr = {'0002':2,'0003':1,'0004':2,'0005':2,'0006':1,'0007':1,'0008':1,'0009':2,'0010':2,
'0011':2,'0012':2,'0013':2,'0014':1,'0015':2,'0016':1,'0017':2,'0018':2,'0019':2,'0020':1,
'0021':2,'0022':1,'0023':1,'0024':1,'0025':1,'0026':1,'0027':1,'0028':1,'0029':2,'0030':1,
'0032':2,'0033':2,'0034':2,'0035':1,'0036':2,'0037':1,'0038':1,'0039':2,'0040':2,
'0041':2,'0042':2,'0043':1,'0044':2,'0045':1,'0046':1,'0047':1,'0048':1,'0049':2,'0050':1,
'0051':1,'0052':1,'0053':2,'0054':2,'0055':2,'0056':1,'0057':1,'0058':1,'0059':1,'0060':2,
'0061':2,'0062':2,'0063':1,
'0071':2,'0072':1,'0073':2
}
if randomSample:
numberOfRandomSamples = 4
randoms = random.sample(range(0, len(AllParticipantsGr)), numberOfRandomSamples)
AllParticipantsGrList = list(AllParticipantsGr)
newDict = {}
for item in randoms:
keys = AllParticipantsGrList[item]
newDict[keys] = AllParticipantsGr.get(keys)
AllParticipantsGr = newDict
if not devMode:
experimentDataList = readAllData(AllParticipantsGr)
ageDF = calculateMeanAge(experimentDataList)
experimentDataListProcessed = getIndexForPhase(experimentDataList)
experimentDataListProcessed = testingHRQualityPerSample(experimentDataListProcessed, showGraphs = True)
meansHR, meansSC = calculateMeansPerPhase(experimentDataListProcessed, thresholds = thresholds)
statisticalResults = statisticalTestsOnData(meansSC, meansHR, thresholds = thresholds, testMode = False)
saveResults(statisticalResults, meansSC, meansHR, ageDF)
if testMode or fullTestMode:
display(statisticalResults)
print(f"\nmain in {time.perf_counter() - start}s")
except Exception as e:
print("error in main")
print(e)
devMode = True
global experimentDataList
if devMode:
AllParticipantsGr = {'0002':2,'0003':1,'0004':2,'0005':2,'0006':1,'0007':1,'0008':1,'0009':2,'0010':2,
'0011':2,'0012':2,'0013':2,'0014':1,'0015':2,'0016':1,'0017':2,'0018':2,'0019':2,'0020':1,
'0021':2,'0022':1,'0023':1,'0024':1,'0025':1,'0026':1,'0027':1,'0028':1,'0029':2,'0030':1,
'0032':2,'0033':2,'0034':2,'0035':1,'0036':2,'0037':1,'0038':1,'0039':2,'0040':2,
'0041':2,'0042':2,'0043':1,'0044':2,'0045':1,'0046':1,'0047':1,'0048':1,'0049':2,'0050':1,
'0051':1,'0052':1,'0053':2,'0054':2,'0055':2,'0056':1,'0057':1,'0058':1,'0059':1,'0060':2,
'0061':2,'0062':2,'0063':1,
'0071':2,'0072':1,'0073':2
}
experimentDataList = readAllData(AllParticipantsGr)
#else: # maybe check if empty ???
#experimentDataList = []
fullTestMode = True
if __name__ == "__main__":
main(experimentDataList, testMode = False, devMode = devMode)
Average participant age : 28.71875 Standard deviation of participant age : 10.479844867053139 Median of participant age : 25.0 N = 64 ________________________________________________________________________________ Mean female age : 29.03125 Standard deviation of female age : 11.114417368332898 Median of female age : 24.0 N = 32 Mean male age : 28.40625 Standard deviation of male age : 9.794320340763825 Median of male age : 25.5 N = 32
calculateMeanAge in 0.029754100018180907s ____________________________________________________________________________________________________ Shortest break : 15.06 Longest break : 82.5 ________________________________________________________________________________ Mean break length : 37.819218750000005 SD break length : 13.889500452757416 ________________________________________ Median break length : 36.905 Q1 and Q3 break : [27.52 44.6 ]
getIndexForPhase in 7.868109200033359s
testingHRQualityPerSample in 0.7257573999813758s ____________________________________________________________________________________________________ meansHR :
| threshold : 0% | threshold : 9% | threshold : 50% | ... | targeted threshold : 90% | Phases lengths | Gender | Age | MSSQ | ID | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| adaptation PE | instruction | break | adaptation VE | adaptation PE | instruction | break | adaptation VE | adaptation PE | instruction | ... | break | adaptation VE | adaptation PE | instruction | break | adaptation VE | |||||
| 0 | 85.236 | 83.382 | 101.932 | 92.675 | 85.236 | 83.382 | 101.932 | 92.675 | 85.236 | 83.382 | ... | NaN | NaN | 90.010 | 15.960 | 38.290 | 89.990 | 1.000 | 21.000 | 29.000 | 2.000 |
| 1 | 101.153 | 102.931 | 118.315 | 109.265 | 101.153 | 102.931 | 118.315 | 109.265 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.940 | 36.890 | 89.990 | 1.000 | 19.000 | 45.000 | 3.000 |
| 2 | 76.114 | 79.260 | 94.969 | 78.355 | 76.114 | 79.260 | 94.969 | 78.355 | 76.114 | 79.260 | ... | NaN | NaN | 90.010 | 15.940 | 15.060 | 89.980 | 1.000 | 42.000 | 41.000 | 4.000 |
| 3 | 90.888 | 99.455 | 102.640 | 89.783 | 90.888 | 99.455 | 102.640 | 89.783 | 90.888 | 99.455 | ... | 102.640 | 89.783 | 90.010 | 15.940 | 28.130 | 89.980 | 1.000 | 23.000 | 0.000 | 5.000 |
| 4 | 73.799 | 87.859 | 86.673 | 74.219 | 73.799 | 87.859 | 86.673 | 74.219 | 73.799 | 87.859 | ... | NaN | 74.219 | 90.010 | 15.950 | 29.340 | 89.980 | 1.000 | 21.000 | 10.000 | 6.000 |
| 5 | 79.555 | 78.763 | 92.231 | 79.915 | 79.555 | 78.763 | 92.231 | 79.915 | 79.555 | 78.763 | ... | 92.231 | 79.915 | 90.000 | 15.950 | 46.820 | 89.980 | 1.000 | 34.000 | 0.000 | 7.000 |
| 6 | 90.530 | 124.432 | 119.617 | 91.481 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 90.010 | 15.930 | 37.060 | 89.990 | 1.000 | 18.000 | 10.000 | 8.000 |
| 7 | 70.301 | 80.795 | 117.689 | 68.245 | 70.301 | 80.795 | 117.689 | 68.245 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.970 | 36.010 | 89.980 | 1.000 | 19.000 | 0.000 | 9.000 |
| 8 | 79.088 | 81.571 | 87.270 | 74.460 | 79.088 | 81.571 | 87.270 | 74.460 | 79.088 | 81.571 | ... | NaN | 74.460 | 90.010 | 15.950 | 33.420 | 89.980 | 1.000 | 37.000 | 0.000 | 10.000 |
| 9 | 70.492 | 79.437 | 79.064 | 68.414 | 70.492 | 79.437 | 79.064 | 68.414 | 70.492 | 79.437 | ... | 79.064 | 68.414 | 90.010 | 15.930 | 37.320 | 89.990 | 1.000 | 36.000 | 29.000 | 11.000 |
| 10 | 59.377 | 60.844 | 83.050 | 59.550 | 59.377 | 60.844 | 83.050 | 59.550 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.950 | 44.600 | 89.980 | 1.000 | 34.000 | 61.000 | 12.000 |
| 11 | 60.795 | 62.545 | 68.301 | 58.271 | 60.795 | 62.545 | 68.301 | 58.271 | 60.795 | 62.545 | ... | NaN | 58.271 | 90.010 | 15.950 | 69.340 | 89.980 | 1.000 | 56.000 | 30.000 | 13.000 |
| 12 | 59.112 | 61.374 | 70.564 | 58.074 | 59.112 | 61.374 | 70.564 | 58.074 | 59.112 | 61.374 | ... | NaN | 58.074 | 90.000 | 15.980 | 34.400 | 89.990 | 1.000 | 32.000 | 81.000 | 14.000 |
| 13 | 71.382 | 85.782 | 97.895 | 98.323 | 71.382 | 85.782 | 97.895 | 98.323 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.950 | 27.220 | 89.980 | 1.000 | 21.000 | 0.000 | 15.000 |
| 14 | 66.578 | 75.552 | 81.535 | 64.677 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 90.010 | 15.960 | 22.860 | 89.990 | 1.000 | 25.000 | 0.000 | 16.000 |
| 15 | 81.326 | 86.189 | 96.499 | 78.064 | 81.326 | 86.189 | 96.499 | 78.064 | 81.326 | 86.189 | ... | NaN | 78.064 | 90.010 | 15.920 | 29.310 | 90.000 | 1.000 | 22.000 | 20.000 | 17.000 |
| 16 | 85.046 | 93.154 | 156.897 | 157.215 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 90.000 | 15.970 | 60.220 | 89.980 | 1.000 | 19.000 | 69.000 | 18.000 |
| 17 | 61.917 | 59.502 | 74.137 | 64.656 | 61.917 | 59.502 | 74.137 | 64.656 | NaN | NaN | ... | NaN | NaN | 90.000 | 15.970 | 38.100 | 89.990 | 1.000 | 36.000 | 45.000 | 19.000 |
| 18 | 108.361 | 115.214 | 133.078 | 104.255 | 108.361 | 115.214 | 133.078 | 104.255 | 108.361 | 115.214 | ... | NaN | 104.255 | 90.000 | 15.970 | 27.750 | 89.990 | 1.000 | 23.000 | 0.000 | 20.000 |
| 19 | 66.279 | 63.632 | 81.327 | 66.092 | 66.279 | 63.632 | 81.327 | 66.092 | 66.279 | 63.632 | ... | NaN | 66.092 | 90.010 | 15.940 | 57.800 | 89.980 | 1.000 | 29.000 | 56.000 | 21.000 |
| 20 | 70.996 | 75.160 | 82.369 | 73.624 | 70.996 | 75.160 | 82.369 | 73.624 | 70.996 | 75.160 | ... | 82.369 | 73.624 | 90.010 | 15.930 | 53.270 | 89.990 | 1.000 | 29.000 | 0.000 | 22.000 |
| 21 | 63.246 | 70.250 | 75.047 | 64.154 | 63.246 | 70.250 | 75.047 | 64.154 | 63.246 | 70.250 | ... | NaN | 64.154 | 90.010 | 15.940 | 58.310 | 89.980 | 1.000 | 55.000 | 10.000 | 23.000 |
| 22 | 75.215 | 74.492 | 82.492 | 73.516 | 75.215 | 74.492 | 82.492 | 73.516 | 75.215 | 74.492 | ... | 82.492 | 73.516 | 90.010 | 15.940 | 82.500 | 89.980 | 1.000 | 53.000 | 89.000 | 24.000 |
| 23 | 91.640 | 104.832 | 112.660 | 92.141 | 91.640 | 104.832 | 112.660 | 92.141 | NaN | NaN | ... | NaN | 92.141 | 90.000 | 15.960 | 29.410 | 89.980 | 1.000 | 41.000 | 0.000 | 25.000 |
| 24 | 73.494 | 74.361 | 87.418 | 75.076 | 73.494 | 74.361 | 87.418 | 75.076 | 73.494 | 74.361 | ... | NaN | 75.076 | 90.010 | 15.960 | 23.420 | 89.970 | 1.000 | 41.000 | 41.000 | 26.000 |
| 25 | 68.106 | 78.410 | 140.570 | 91.677 | 68.106 | 78.410 | 140.570 | 91.677 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.940 | 25.500 | 89.990 | 1.000 | 21.000 | 51.000 | 27.000 |
| 26 | 91.405 | 93.378 | 128.038 | 88.546 | 91.405 | 93.378 | 128.038 | 88.546 | NaN | NaN | ... | NaN | 88.546 | 90.010 | 15.950 | 33.320 | 89.980 | 1.000 | 19.000 | 35.000 | 28.000 |
| 27 | 90.982 | 90.850 | 102.663 | 92.380 | 90.982 | 90.850 | 102.663 | 92.380 | 90.982 | 90.850 | ... | NaN | 92.380 | 90.010 | 15.940 | 40.290 | 89.980 | 1.000 | 19.000 | 37.000 | 29.000 |
| 28 | 103.072 | 86.465 | 94.188 | 88.042 | 103.072 | 86.465 | 94.188 | 88.042 | NaN | NaN | ... | NaN | NaN | 90.020 | 15.950 | 51.160 | 89.980 | 1.000 | 19.000 | 0.000 | 30.000 |
| 29 | 75.621 | 70.590 | 97.271 | 77.279 | 75.621 | 70.590 | 97.271 | 77.279 | 75.621 | 70.590 | ... | NaN | 77.279 | 90.010 | 15.960 | 29.230 | 89.990 | 0.000 | 30.000 | 0.000 | 32.000 |
| 30 | 87.902 | 99.117 | 107.438 | 93.139 | 87.902 | 99.117 | 107.438 | 93.139 | NaN | NaN | ... | NaN | 93.139 | 90.010 | 15.930 | 39.320 | 89.990 | 0.000 | 20.000 | 49.000 | 33.000 |
| 31 | 63.758 | 78.770 | 94.701 | 55.395 | 63.758 | 78.770 | 94.701 | 55.395 | 63.758 | 78.770 | ... | NaN | 55.395 | 90.000 | 15.960 | 24.580 | 89.970 | 0.000 | 20.000 | 11.000 | 34.000 |
| 32 | 84.639 | 86.959 | 92.363 | 86.833 | 84.639 | 86.959 | 92.363 | 86.833 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.940 | 26.760 | 89.980 | 0.000 | 20.000 | 29.000 | 35.000 |
| 33 | 63.586 | 65.042 | 78.221 | 63.294 | 63.586 | 65.042 | 78.221 | 63.294 | NaN | NaN | ... | NaN | 63.294 | 90.000 | 15.980 | 33.690 | 89.990 | 0.000 | 27.000 | 10.000 | 36.000 |
| 34 | 84.891 | 84.008 | 86.258 | 82.967 | 84.891 | 84.008 | 86.258 | 82.967 | 84.891 | 84.008 | ... | NaN | 82.967 | 90.010 | 15.950 | 27.620 | 89.980 | 0.000 | 19.000 | 45.000 | 37.000 |
| 35 | 79.653 | 84.339 | 103.334 | 176.380 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 89.990 | 15.950 | 30.080 | 89.980 | 0.000 | 19.000 | 33.000 | 38.000 |
| 36 | 92.462 | 93.509 | 112.415 | 96.702 | 92.462 | 93.509 | 112.415 | 96.702 | 92.462 | 93.509 | ... | NaN | 96.702 | 90.010 | 15.930 | 57.560 | 89.980 | 0.000 | 23.000 | 0.000 | 39.000 |
| 37 | 71.710 | 70.785 | 83.848 | 74.510 | 71.710 | 70.785 | 83.848 | 74.510 | 71.710 | 70.785 | ... | NaN | NaN | 90.000 | 15.960 | 21.540 | 89.990 | 0.000 | 34.000 | 5.000 | 40.000 |
| 38 | 79.336 | 86.487 | 98.962 | 79.810 | 79.336 | 86.487 | 98.962 | 79.810 | 79.336 | 86.487 | ... | NaN | 79.810 | 90.010 | 15.970 | 19.830 | 89.980 | 0.000 | 41.000 | 20.000 | 41.000 |
| 39 | 76.486 | 78.736 | 110.618 | 94.243 | 76.486 | 78.736 | 110.618 | 94.243 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.930 | 39.400 | 89.980 | 0.000 | 19.000 | 10.000 | 42.000 |
| 40 | 57.317 | 57.628 | 64.330 | 57.190 | 57.317 | 57.628 | 64.330 | 57.190 | 57.317 | 57.628 | ... | NaN | 57.190 | 90.000 | 15.940 | 17.010 | 89.980 | 0.000 | 59.000 | 29.000 | 43.000 |
| 41 | 76.426 | 80.515 | 124.975 | 91.202 | 76.426 | 80.515 | 124.975 | 91.202 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.930 | 23.690 | 89.990 | 0.000 | 34.000 | 44.000 | 44.000 |
| 42 | 78.259 | 80.652 | 88.259 | 74.167 | 78.259 | 80.652 | 88.259 | 74.167 | 78.259 | 80.652 | ... | NaN | 74.167 | 90.000 | 15.980 | 50.000 | 89.980 | 0.000 | 35.000 | 0.000 | 45.000 |
| 43 | 111.003 | 112.192 | 174.618 | 164.242 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 90.000 | 15.960 | 43.360 | 89.980 | 0.000 | 47.000 | 0.000 | 46.000 |
| 44 | 57.534 | 63.060 | 67.108 | 57.773 | 57.534 | 63.060 | 67.108 | 57.773 | 57.534 | 63.060 | ... | NaN | 57.773 | 90.010 | 15.950 | 36.920 | 89.990 | 0.000 | 35.000 | 0.000 | 47.000 |
| 45 | 80.055 | 106.873 | 78.157 | 69.742 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 89.980 | 15.940 | 44.600 | 89.980 | 0.000 | 22.000 | 10.000 | 48.000 |
| 46 | 69.141 | 74.775 | 78.541 | 72.868 | 69.141 | 74.775 | 78.541 | 72.868 | 69.141 | 74.775 | ... | NaN | 72.868 | 90.000 | 15.940 | 34.340 | 89.990 | 0.000 | 33.000 | 0.000 | 49.000 |
| 47 | 62.515 | 62.301 | 115.804 | 59.010 | 62.515 | 62.301 | 115.804 | 59.010 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.950 | 31.830 | 89.990 | 0.000 | 29.000 | 66.000 | 50.000 |
| 48 | 66.346 | 71.146 | 127.744 | 67.197 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | 67.197 | 90.010 | 15.950 | 37.620 | 89.980 | 0.000 | 33.000 | 0.000 | 51.000 |
| 49 | 78.854 | 87.195 | 112.294 | 75.585 | 78.854 | 87.195 | 112.294 | 75.585 | NaN | NaN | ... | NaN | 75.585 | 90.010 | 15.930 | 40.740 | 89.990 | 0.000 | 25.000 | 0.000 | 52.000 |
| 50 | 70.607 | 70.246 | 78.726 | 69.529 | 70.607 | 70.246 | 78.726 | 69.529 | 70.607 | 70.246 | ... | 78.726 | 69.529 | 90.010 | 15.960 | 23.610 | 89.980 | 0.000 | 25.000 | 59.000 | 53.000 |
| 51 | 62.050 | 65.298 | 72.020 | 60.224 | 62.050 | 65.298 | 72.020 | 60.224 | 62.050 | 65.298 | ... | NaN | 60.224 | 90.010 | 15.920 | 20.800 | 89.990 | 0.000 | 50.000 | 5.000 | 54.000 |
| 52 | 82.003 | 82.152 | 85.867 | 74.750 | 82.003 | 82.152 | 85.867 | 74.750 | 82.003 | 82.152 | ... | NaN | 74.750 | 90.010 | 15.950 | 24.220 | 89.980 | 0.000 | 30.000 | 5.000 | 55.000 |
| 53 | 69.640 | 75.352 | 80.205 | 71.341 | 69.640 | 75.352 | 80.205 | 71.341 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.940 | 24.650 | 89.970 | 0.000 | 31.000 | 0.000 | 56.000 |
| 54 | 81.271 | 78.429 | 84.813 | 73.274 | 81.271 | 78.429 | 84.813 | 73.274 | NaN | NaN | ... | NaN | 73.274 | 90.010 | 15.960 | 25.870 | 89.990 | 0.000 | 21.000 | 10.000 | 57.000 |
| 55 | 72.445 | 75.038 | 98.079 | 68.687 | 72.445 | 75.038 | 98.079 | 68.687 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.930 | 47.130 | 89.980 | 0.000 | 19.000 | 0.000 | 58.000 |
| 56 | 58.405 | 60.180 | 66.696 | 64.070 | 58.405 | 60.180 | 66.696 | 64.070 | 58.405 | 60.180 | ... | NaN | NaN | 90.010 | 15.960 | 61.980 | 89.980 | 0.000 | 20.000 | 10.000 | 59.000 |
| 57 | 69.552 | 75.274 | 108.314 | 78.068 | 69.552 | 75.274 | 108.314 | 78.068 | NaN | NaN | ... | NaN | NaN | 90.010 | 15.960 | 37.190 | 89.980 | 0.000 | 22.000 | 5.000 | 60.000 |
| 58 | 100.218 | 112.117 | 129.842 | 101.955 | 100.218 | 112.117 | 129.842 | 101.955 | 100.218 | 112.117 | ... | NaN | 101.955 | 90.010 | 15.930 | 42.980 | 89.980 | 1.000 | 25.000 | 10.000 | 61.000 |
| 59 | 70.417 | 75.459 | 86.857 | 74.112 | 70.417 | 75.459 | 86.857 | 74.112 | 70.417 | 75.459 | ... | 86.857 | 74.112 | 90.010 | 15.920 | 44.720 | 89.980 | 1.000 | 21.000 | 10.000 | 62.000 |
| 60 | 97.476 | 96.021 | 107.763 | 90.689 | 97.476 | 96.021 | 107.763 | 90.689 | 97.476 | 96.021 | ... | 107.763 | 90.689 | 90.010 | 15.960 | 65.550 | 89.990 | 1.000 | 19.000 | 0.000 | 63.000 |
| 61 | 75.240 | 76.567 | 89.926 | 75.612 | 75.240 | 76.567 | 89.926 | 75.612 | 75.240 | 76.567 | ... | 89.926 | 75.612 | 90.010 | 15.920 | 37.480 | 89.990 | 0.000 | 21.000 | 10.000 | 71.000 |
| 62 | 84.669 | 85.627 | 95.045 | 84.156 | 84.669 | 85.627 | 95.045 | 84.156 | NaN | NaN | ... | NaN | 84.156 | 90.010 | 15.940 | 40.590 | 89.980 | 0.000 | 26.000 | 10.000 | 72.000 |
| 63 | 182.615 | 142.365 | 102.136 | 64.389 | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 90.010 | 15.950 | 66.820 | 89.980 | 0.000 | 20.000 | 0.000 | 73.000 |
64 rows × 40 columns
____________________________________________________________________________________________________ meansSC :
| Skin Conductance | Phasic Component | Tonic Component | Phases lengths | Gender | Age | MSSQ | ID | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| adaptation PE | instruction | break | adaptation VE | adaptation PE | instruction | break | adaptation VE | adaptation PE | instruction | break | adaptation VE | adaptation PE | instruction | break | adaptation VE | |||||
| 0 | 1.558 | 1.302 | 1.332 | 1.425 | 0.081 | 0.162 | 0.576 | 0.157 | 0.839 | -0.395 | -0.675 | 0.164 | 90.010 | 15.960 | 38.290 | 89.990 | 1.000 | 21.000 | 29.000 | 2.000 |
| 1 | 1.989 | 2.130 | 2.049 | 2.022 | 0.112 | 0.680 | 0.618 | 0.214 | 0.757 | 0.690 | 0.465 | 0.774 | 90.010 | 15.940 | 36.890 | 89.990 | 1.000 | 19.000 | 45.000 | 3.000 |
| 2 | 1.133 | 1.169 | 1.394 | 1.173 | 0.076 | 1.208 | 2.151 | 0.059 | 0.837 | -0.159 | -0.258 | 1.005 | 90.010 | 15.940 | 15.060 | 89.980 | 1.000 | 42.000 | 41.000 | 4.000 |
| 3 | 3.131 | 3.473 | 4.491 | 4.543 | 0.134 | 0.593 | 1.287 | 0.707 | -1.497 | -1.408 | -0.471 | 0.192 | 90.010 | 15.940 | 28.130 | 89.980 | 1.000 | 23.000 | 0.000 | 5.000 |
| 4 | 1.960 | 2.055 | 3.132 | 2.220 | 0.020 | 0.393 | 0.761 | 0.025 | 0.390 | 0.212 | 2.054 | 0.918 | 90.010 | 15.950 | 29.340 | 89.980 | 1.000 | 21.000 | 10.000 | 6.000 |
| 5 | 1.346 | 1.397 | 1.839 | 1.739 | 0.108 | 0.476 | 0.971 | 0.104 | -0.341 | -0.506 | 0.753 | 1.224 | 90.000 | 15.950 | 46.820 | 89.980 | 1.000 | 34.000 | 0.000 | 7.000 |
| 6 | 2.904 | 2.647 | 2.653 | 2.580 | 0.081 | 0.047 | 0.139 | 0.077 | 1.246 | 0.788 | 0.706 | 0.630 | 90.010 | 15.930 | 37.060 | 89.990 | 1.000 | 18.000 | 10.000 | 8.000 |
| 7 | 0.356 | 0.313 | 0.467 | 0.431 | 0.014 | 0.076 | 0.595 | 0.005 | 0.644 | 0.162 | 1.147 | 1.391 | 90.010 | 15.970 | 36.010 | 89.980 | 1.000 | 19.000 | 0.000 | 9.000 |
| 8 | 2.158 | 1.979 | 1.953 | 1.955 | 0.044 | 0.289 | 4.055 | 0.037 | 1.602 | 0.655 | -3.209 | 0.817 | 90.010 | 15.950 | 33.420 | 89.980 | 1.000 | 37.000 | 0.000 | 10.000 |
| 9 | 1.988 | 2.266 | 2.770 | 2.443 | 0.034 | 0.403 | 0.430 | 0.056 | 0.667 | 0.678 | 1.341 | 1.268 | 90.010 | 15.930 | 37.320 | 89.990 | 1.000 | 36.000 | 29.000 | 11.000 |
| 10 | 1.394 | 1.259 | 1.362 | 1.642 | 0.043 | 0.135 | 0.401 | 0.064 | -1.143 | -1.810 | -1.638 | -0.103 | 90.010 | 15.950 | 44.600 | 89.980 | 1.000 | 34.000 | 61.000 | 12.000 |
| 11 | 0.904 | 0.895 | 0.993 | 0.998 | 0.026 | 0.398 | 3.696 | 0.062 | 0.307 | -0.153 | -2.408 | 1.277 | 90.010 | 15.950 | 69.340 | 89.980 | 1.000 | 56.000 | 30.000 | 13.000 |
| 12 | 1.612 | 1.571 | 1.818 | 1.983 | 0.074 | 0.849 | 5.836 | 0.328 | 0.305 | -0.610 | -4.764 | 1.305 | 90.000 | 15.980 | 34.400 | 89.990 | 1.000 | 32.000 | 81.000 | 14.000 |
| 13 | 2.199 | 2.298 | 2.330 | 2.780 | 0.148 | 0.669 | 2.393 | 0.392 | -0.655 | -0.869 | -2.497 | 0.896 | 90.010 | 15.950 | 27.220 | 89.980 | 1.000 | 21.000 | 0.000 | 15.000 |
| 14 | 4.287 | 3.873 | 3.976 | 3.613 | 0.293 | 2.883 | 7.547 | 0.575 | 1.286 | -1.958 | -6.459 | -0.061 | 90.010 | 15.960 | 22.860 | 89.990 | 1.000 | 25.000 | 0.000 | 16.000 |
| 15 | 4.503 | 4.803 | 5.273 | 4.993 | 0.379 | 0.427 | 0.404 | 0.091 | 0.203 | 0.457 | 0.955 | 0.986 | 90.010 | 15.920 | 29.310 | 90.000 | 1.000 | 22.000 | 20.000 | 17.000 |
| 16 | 1.154 | 0.989 | 1.489 | 1.502 | 0.050 | 0.062 | 0.526 | 0.051 | -0.043 | -0.690 | 0.770 | 1.296 | 90.000 | 15.970 | 60.220 | 89.980 | 1.000 | 19.000 | 69.000 | 18.000 |
| 17 | 1.364 | 0.956 | 0.840 | 0.890 | 0.031 | 0.136 | 0.700 | 0.057 | 1.972 | 0.442 | -0.527 | 0.290 | 90.000 | 15.970 | 38.100 | 89.990 | 1.000 | 36.000 | 45.000 | 19.000 |
| 18 | 2.667 | 2.968 | 4.469 | 2.808 | 0.053 | 0.533 | 1.011 | 0.016 | 0.856 | 0.646 | 1.509 | 1.020 | 90.000 | 15.970 | 27.750 | 89.990 | 1.000 | 23.000 | 0.000 | 20.000 |
| 19 | 1.065 | 1.009 | 1.343 | 1.409 | 0.202 | 0.038 | 4.043 | 0.767 | -1.787 | -2.065 | -3.462 | 0.334 | 90.010 | 15.940 | 57.800 | 89.980 | 1.000 | 29.000 | 56.000 | 21.000 |
| 20 | 0.777 | 0.682 | 1.043 | 1.172 | 0.333 | 1.153 | 4.438 | 0.110 | -0.578 | -1.801 | -3.564 | 1.308 | 90.010 | 15.930 | 53.270 | 89.990 | 1.000 | 29.000 | 0.000 | 22.000 |
| 21 | 4.003 | 4.676 | 4.189 | 4.410 | 0.495 | 1.989 | 0.977 | 0.550 | 0.091 | -0.530 | -0.150 | 0.563 | 90.010 | 15.940 | 58.310 | 89.980 | 1.000 | 55.000 | 10.000 | 23.000 |
| 22 | 1.536 | 1.142 | 1.329 | 1.199 | 0.003 | 0.041 | 0.121 | 0.001 | 1.576 | 0.460 | 0.892 | 0.656 | 90.010 | 15.940 | 82.500 | 89.980 | 1.000 | 53.000 | 89.000 | 24.000 |
| 23 | 1.888 | 1.812 | 2.775 | 2.048 | 0.080 | 1.237 | 1.241 | 0.062 | 0.565 | -0.759 | 1.363 | 0.937 | 90.000 | 15.960 | 29.410 | 89.980 | 1.000 | 41.000 | 0.000 | 25.000 |
| 24 | 4.375 | 4.164 | 6.932 | 4.268 | 0.068 | 0.441 | 0.958 | 0.072 | 0.923 | 0.405 | 1.782 | 0.846 | 90.010 | 15.960 | 23.420 | 89.970 | 1.000 | 41.000 | 41.000 | 26.000 |
| 25 | 1.356 | 1.306 | 1.784 | 1.752 | 0.083 | 0.384 | 1.899 | 0.473 | -0.828 | -1.355 | -0.706 | 0.576 | 90.010 | 15.940 | 25.500 | 89.990 | 1.000 | 21.000 | 51.000 | 27.000 |
| 26 | 3.230 | 3.727 | 4.812 | 3.437 | 0.045 | 0.389 | 0.794 | 0.041 | 0.702 | 0.832 | 1.459 | 0.904 | 90.010 | 15.950 | 33.320 | 89.980 | 1.000 | 19.000 | 35.000 | 28.000 |
| 27 | 1.974 | 2.109 | 2.253 | 2.367 | 0.249 | 1.041 | 0.440 | 0.149 | 0.361 | -0.170 | 0.712 | 1.223 | 90.010 | 15.940 | 40.290 | 89.980 | 1.000 | 19.000 | 37.000 | 29.000 |
| 28 | 5.267 | 5.451 | 6.971 | 6.766 | 0.216 | 0.403 | 1.378 | 0.340 | -1.670 | -1.591 | -0.368 | 0.373 | 90.020 | 15.950 | 51.160 | 89.980 | 1.000 | 19.000 | 0.000 | 30.000 |
| 29 | 1.582 | 1.662 | 1.779 | 1.851 | 0.317 | 0.612 | 2.589 | 0.620 | -1.684 | -1.260 | -2.183 | 0.442 | 90.010 | 15.960 | 29.230 | 89.990 | 0.000 | 30.000 | 0.000 | 32.000 |
| 30 | 1.090 | 1.205 | 1.248 | 1.289 | 1.208 | 1.011 | 2.112 | 0.561 | -2.984 | -1.353 | -1.918 | 0.156 | 90.010 | 15.930 | 39.320 | 89.990 | 0.000 | 20.000 | 49.000 | 33.000 |
| 31 | 1.527 | 1.553 | 1.415 | 1.460 | 0.708 | 8.591 | 24.643 | 0.744 | -1.024 | -8.600 | -26.269 | -1.850 | 90.000 | 15.960 | 24.580 | 89.970 | 0.000 | 20.000 | 11.000 | 34.000 |
| 32 | 2.678 | 3.021 | 2.999 | 3.022 | 0.146 | 2.137 | 4.752 | 0.264 | 0.519 | -0.885 | -3.539 | 0.988 | 90.010 | 15.940 | 26.760 | 89.980 | 0.000 | 20.000 | 29.000 | 35.000 |
| 33 | 0.799 | 0.746 | 0.980 | 1.006 | 0.046 | 0.125 | 1.251 | 0.117 | 0.523 | 0.189 | 0.189 | 1.447 | 90.000 | 15.980 | 33.690 | 89.990 | 0.000 | 27.000 | 10.000 | 36.000 |
| 34 | 6.765 | 7.206 | 8.117 | 7.478 | 0.304 | 0.495 | 0.843 | 0.058 | 0.154 | 0.387 | 0.914 | 1.085 | 90.010 | 15.950 | 27.620 | 89.980 | 0.000 | 19.000 | 45.000 | 37.000 |
| 35 | 5.894 | 6.239 | 6.807 | 6.822 | 0.430 | 0.654 | 1.609 | 0.342 | -1.471 | -1.353 | -1.743 | -0.462 | 89.990 | 15.950 | 30.080 | 89.980 | 0.000 | 19.000 | 33.000 | 38.000 |
| 36 | 1.324 | 1.356 | 1.577 | 1.718 | 0.127 | 0.497 | 0.858 | 0.072 | -2.070 | -2.277 | -1.513 | -0.011 | 90.010 | 15.930 | 57.560 | 89.980 | 0.000 | 23.000 | 0.000 | 39.000 |
| 37 | 0.434 | 0.414 | 0.380 | 0.375 | 0.374 | 0.841 | 15.722 | 0.371 | 0.381 | -0.788 | -16.875 | -1.689 | 90.000 | 15.960 | 21.540 | 89.990 | 0.000 | 34.000 | 5.000 | 40.000 |
| 38 | 3.549 | 4.685 | 4.904 | 4.283 | 0.181 | 1.410 | 2.045 | 0.675 | -1.112 | -0.392 | -0.652 | -0.347 | 90.010 | 15.970 | 19.830 | 89.980 | 0.000 | 41.000 | 20.000 | 41.000 |
| 39 | 1.516 | 1.527 | 1.626 | 1.645 | 1.692 | 1.357 | 11.379 | 3.308 | -3.203 | -2.772 | -11.889 | -3.647 | 90.010 | 15.930 | 39.400 | 89.980 | 0.000 | 19.000 | 10.000 | 42.000 |
| 40 | 1.943 | 1.887 | 2.665 | 2.602 | 0.035 | 0.204 | 0.567 | 0.115 | 0.256 | -0.021 | 1.104 | 1.437 | 90.000 | 15.940 | 17.010 | 89.980 | 0.000 | 59.000 | 29.000 | 43.000 |
| 41 | 1.798 | 1.870 | 1.713 | 2.221 | 0.099 | 2.199 | 30.598 | 3.625 | -0.289 | -2.087 | -31.147 | -2.031 | 90.010 | 15.930 | 23.690 | 89.990 | 0.000 | 34.000 | 44.000 | 44.000 |
| 42 | 2.149 | 2.071 | 2.267 | 1.897 | 0.133 | 1.331 | 4.897 | 0.043 | 1.210 | -0.364 | -2.984 | 0.084 | 90.000 | 15.980 | 50.000 | 89.980 | 0.000 | 35.000 | 0.000 | 45.000 |
| 43 | 1.626 | 1.350 | 2.432 | 2.430 | 0.022 | 0.287 | 0.778 | 0.206 | 0.242 | -0.464 | 0.776 | 1.345 | 90.000 | 15.960 | 43.360 | 89.980 | 0.000 | 47.000 | 0.000 | 46.000 |
| 44 | 2.264 | 2.637 | 3.454 | 2.673 | 0.115 | 1.323 | 1.431 | 0.289 | -1.251 | -1.318 | 1.081 | -0.171 | 90.010 | 15.950 | 36.920 | 89.990 | 0.000 | 35.000 | 0.000 | 47.000 |
| 45 | 1.566 | 1.588 | 1.591 | 1.657 | 3.382 | 3.169 | 24.385 | 0.820 | -4.794 | -4.034 | -25.172 | 0.060 | 89.980 | 15.940 | 44.600 | 89.980 | 0.000 | 22.000 | 10.000 | 48.000 |
| 46 | 4.024 | 3.952 | 4.166 | 3.905 | 0.278 | 0.772 | 0.929 | 0.272 | 0.144 | -0.587 | -0.032 | -0.242 | 90.000 | 15.940 | 34.340 | 89.990 | 0.000 | 33.000 | 0.000 | 49.000 |
| 47 | 1.679 | 1.577 | 1.826 | 1.773 | 0.344 | 0.938 | 1.945 | 0.237 | 0.590 | -0.270 | -0.631 | 0.940 | 90.010 | 15.950 | 31.830 | 89.990 | 0.000 | 29.000 | 66.000 | 50.000 |
| 48 | 2.647 | 3.089 | 3.957 | 3.033 | 0.005 | 0.951 | 0.750 | 0.037 | 0.336 | 0.103 | 1.703 | 0.928 | 90.010 | 15.950 | 37.620 | 89.980 | 0.000 | 33.000 | 0.000 | 51.000 |
| 49 | 2.877 | 3.068 | 3.475 | 3.406 | 0.070 | 0.536 | 0.750 | 0.095 | 0.128 | 0.022 | 0.573 | 1.098 | 90.010 | 15.930 | 40.740 | 89.990 | 0.000 | 25.000 | 0.000 | 52.000 |
| 50 | 1.179 | 1.108 | 1.118 | 1.147 | 0.313 | 2.456 | 1.198 | 0.892 | 1.138 | -2.532 | -1.071 | -0.131 | 90.010 | 15.960 | 23.610 | 89.980 | 0.000 | 25.000 | 59.000 | 53.000 |
| 51 | 3.015 | 3.490 | 3.910 | 3.011 | 0.225 | 12.400 | 8.700 | 0.242 | -0.624 | -11.412 | -6.488 | -0.652 | 90.010 | 15.920 | 20.800 | 89.990 | 0.000 | 50.000 | 5.000 | 54.000 |
| 52 | 1.453 | 1.440 | 1.480 | 1.521 | 0.040 | 0.995 | 1.303 | 0.234 | 0.797 | -0.259 | -0.254 | 1.128 | 90.010 | 15.950 | 24.220 | 89.980 | 0.000 | 30.000 | 5.000 | 55.000 |
| 53 | 0.934 | 0.964 | 1.113 | 1.111 | 0.161 | 0.662 | 0.888 | 0.335 | 0.112 | -0.191 | 0.579 | 1.118 | 90.010 | 15.940 | 24.650 | 89.970 | 0.000 | 31.000 | 0.000 | 56.000 |
| 54 | 5.056 | 5.342 | 5.405 | 4.904 | 0.262 | 0.794 | 0.676 | 0.200 | -1.119 | -1.072 | -0.826 | -1.366 | 90.010 | 15.960 | 25.870 | 89.990 | 0.000 | 21.000 | 10.000 | 57.000 |
| 55 | 1.772 | 1.657 | 1.726 | 1.509 | 0.020 | 0.445 | 1.828 | 0.093 | 1.533 | 0.655 | -0.456 | 0.420 | 90.010 | 15.930 | 47.130 | 89.980 | 0.000 | 19.000 | 0.000 | 58.000 |
| 56 | 0.710 | 0.655 | 1.099 | 0.982 | 0.026 | 0.208 | 0.314 | 0.091 | -0.169 | -0.647 | 1.629 | 1.225 | 90.010 | 15.960 | 61.980 | 89.980 | 0.000 | 20.000 | 10.000 | 59.000 |
| 57 | 6.654 | 6.132 | 7.118 | 6.761 | 0.030 | 0.102 | 0.469 | 0.033 | 0.951 | 0.366 | 0.969 | 1.053 | 90.010 | 15.960 | 37.190 | 89.980 | 0.000 | 22.000 | 5.000 | 60.000 |
| 58 | 4.794 | 4.795 | 5.050 | 5.626 | 0.404 | 1.378 | 2.626 | 0.496 | -1.069 | -2.041 | -2.855 | 0.257 | 90.010 | 15.930 | 42.980 | 89.980 | 1.000 | 25.000 | 10.000 | 61.000 |
| 59 | 4.832 | 4.543 | 6.456 | 5.628 | 0.302 | 0.508 | 0.851 | 0.279 | -0.136 | -0.635 | 0.970 | 0.699 | 90.010 | 15.920 | 44.720 | 89.980 | 1.000 | 21.000 | 10.000 | 62.000 |
| 60 | 9.036 | 9.185 | 9.305 | 9.226 | 0.183 | 0.395 | 2.971 | 0.107 | -0.974 | -0.799 | -3.064 | -0.406 | 90.010 | 15.960 | 65.550 | 89.990 | 1.000 | 19.000 | 0.000 | 63.000 |
| 61 | 2.497 | 2.230 | 2.629 | 2.731 | 0.195 | 15.850 | 23.912 | 1.518 | -0.296 | -16.806 | -23.590 | -0.869 | 90.010 | 15.920 | 37.480 | 89.990 | 0.000 | 21.000 | 10.000 | 71.000 |
| 62 | 3.222 | 2.998 | 3.496 | 3.288 | 0.084 | 0.204 | 0.421 | 0.158 | 0.831 | 0.414 | 0.857 | 0.844 | 90.010 | 15.940 | 40.590 | 89.980 | 0.000 | 26.000 | 10.000 | 72.000 |
| 63 | 1.263 | 1.222 | 1.409 | 1.848 | 0.031 | 0.283 | 0.318 | 0.096 | -1.098 | -1.514 | -0.798 | 1.196 | 90.010 | 15.950 | 66.820 | 89.980 | 0.000 | 20.000 | 0.000 | 73.000 |
calculateMeansPerPhase in 0.6135291999671608s
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for adaptation PE - instruction : -0.04158676492803516
standard deviation for differences in Skin Conductance for adaptation PE - instruction : 0.2762758884200016
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9399993419647217, pvalue=0.00379479443654418)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.8554775596213147, pvalue=0.0042972159502872274)
__________________________________________________
Skew : -1.0244087325216287
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-1.1947648250507066, pvalue=0.23665753802076023) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=943.0, pvalue=0.5165400906836031) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Skin Conductance : pearson r = 0.22495728455619718 (p = 0.07390967004398297) regressive line first order : y = 0.004474622673342874 * x + -0.21081349863489893
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Skin Conductance : pearson r = -0.049799036167205685 (p = 0.6959577028131353) regressive line first order : y = -0.001312831738836924 * x + -0.0038838784283122324
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Skin Conductance : pearson r = 0.1166206671269302 (p = 0.3587686259476603) regressive line first order : y = 0.0013833121705076681 * x + -0.06933946534884519
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for adaptation PE - instruction : -1.0591250391742832
standard deviation for differences in Phasic Component for adaptation PE - instruction : 2.5674681440788514
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.3989065885543823, pvalue=9.638019447474628e-15)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.78967274344623, pvalue=7.052369382028885e-09)
__________________________________________________
Skew : -4.409717522049862
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-3.2742546007113793, pvalue=0.0017232559817109956) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=78.0, pvalue=1.2479751925932432e-10) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Phasic Component : pearson r = 0.20424592923737012 (p = 0.10547323696516059) regressive line first order : y = 0.03775477157428116 * x + -2.4869810041983027
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Phasic Component : pearson r = -0.07909605369914664 (p = 0.5344209316120916) regressive line first order : y = -0.019377824841028707 * x + -0.5026181320209892
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Phasic Component : pearson r = 0.11124599184799833 (p = 0.3814932897058098) regressive line first order : y = 0.012262841884288821 * x + -1.3051483044778267
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for adaptation PE - instruction : 1.047740686699888
standard deviation for differences in Tonic Component for adaptation PE - instruction : 2.6030403832127345
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.477039635181427, pvalue=9.61008480909395e-14)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.827463014688632, pvalue=5.627631665127431e-09)
__________________________________________________
Skew : 4.311035898718172
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=3.194796338968739, pvalue=0.0021867580101948892) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=267.0, pvalue=2.347950751147718e-07) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Tonic Component : pearson r = -0.14817049470077284 (p = 0.24261946914781266) regressive line first order : y = -0.027768729524764904 * x + 2.097932343006554
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Tonic Component : pearson r = 0.07086432527355602 (p = 0.5779090828136) regressive line first order : y = 0.017601663264702406 * x + 0.5422429198167152
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Tonic Component : pearson r = -0.055420143551086376 (p = 0.6635933434764806) regressive line first order : y = -0.006193701119781962 * x + 1.172001815415513
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for adaptation PE - instruction : -3.6732210499662887
standard deviation for differences in HR[1] for adaptation PE - instruction : 8.921645545953382
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.792171835899353, pvalue=4.215728566236976e-08)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.811382424427113, pvalue=1.4988990161357825e-06)
__________________________________________________
Skew : 1.0679396011288729
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-3.2679272087377584, pvalue=0.0017564743365040298) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=329.0, pvalue=1.986273900017716e-06) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Heart Rate(HR)[threshold : 0%] : pearson r = 0.2690497273620118 (p = 0.03157198592072313) regressive line first order : y = 0.17281876406740967 * x + -10.209091692336289
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Heart Rate(HR)[threshold : 0%] : pearson r = 0.05967598751941125 (p = 0.6394868343185006) regressive line first order : y = 0.050803042889186724 * x + -5.132220937940118
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Heart Rate(HR)[threshold : 0%] : pearson r = 0.056027226319201776 (p = 0.6601330477386912) regressive line first order : y = 0.02146079371309181 * x + -4.103778223835191
<ipython-input-302-8d36e867c83d>:249: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig8, ax8 = plt.subplots(figsize=(8, 8))
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for adaptation PE - instruction : -3.336788808609637
standard deviation for differences in HR[0.9] for adaptation PE - instruction : 5.4327989383880615
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9357489347457886, pvalue=0.005134544335305691)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.453882585511608, pvalue=0.014132311729913537)
__________________________________________________
Skew : 0.26374170634815264
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-4.554979559304449, pvalue=2.950917612307009e-05) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=252.0, pvalue=8.437623175505592e-06) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Heart Rate(HR)[threshold : 9%] : pearson r = 0.24009338984425913 (p = 0.07469346107750645) regressive line first order : y = 0.09474414051389106 * x + -6.852151712201922
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Heart Rate(HR)[threshold : 9%] : pearson r = 0.043113575945613956 (p = 0.7523802090776602) regressive line first order : y = 0.022216089701716724 * x + -3.9854192847222576
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Heart Rate(HR)[threshold : 9%] : pearson r = 0.17337839953985537 (p = 0.2012898616955424) regressive line first order : y = 0.04048170356129745 * x + -4.17678415750656
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for adaptation PE - instruction : -3.1621240539980255
standard deviation for differences in HR[0.5] for adaptation PE - instruction : 4.573568469061766
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9403843879699707, pvalue=0.057486630976200104)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.8825252382756238, pvalue=0.37749283772417097)
__________________________________________________
Skew : -0.8355236138588078
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-4.031467631702068, pvalue=0.0002958639235468706) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=103.0, pvalue=0.0005158688241665825) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Heart Rate(HR)[threshold : 50%] : pearson r = 0.1968306617501258 (p = 0.2570859502156249) regressive line first order : y = 0.05538088975217651 * x + -5.289921130722076
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Heart Rate(HR)[threshold : 50%] : pearson r = 0.10345082162443539 (p = 0.5542614174839909) regressive line first order : y = 0.040944670616358245 * x + -4.4641645795982186
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Heart Rate(HR)[threshold : 50%] : pearson r = 0.27290741372595395 (p = 0.11269771529413267) regressive line first order : y = 0.053301874690654505 * x + -4.215978262167536
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for adaptation PE - instruction _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for adaptation PE - instruction : -2.5514685245712427
standard deviation for differences in HR[0.1] for adaptation PE - instruction : 3.759161597984916
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9176590442657471, pvalue=0.5149521231651306)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=-0.5189623974263257, pvalue=0.6037869635562181)
__________________________________________________
Skew : -0.49634963650788416
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for adaptation PE - instruction : Ttest results : Ttest_relResult(statistic=-1.3574667957551745, pvalue=0.24616979854242724) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=4.0, pvalue=0.4375) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - instruction in Heart Rate(HR)[threshold : 90%] : pearson r = 0.8036867914396143 (p = 0.10128312462091907) regressive line first order : y = 0.153104265213092 * x + -10.463284533722984
____________________________________________________________________________________________________ correlation for Age and adaptation PE - instruction in Heart Rate(HR)[threshold : 90%] : pearson r = 0.3504616275117042 (p = 0.5630889466320482) regressive line first order : y = 0.10242623478029385 * x + -5.3579473575512955
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - instruction in Heart Rate(HR)[threshold : 90%] : pearson r = 0.43750588002602125 (p = 0.46126992418116863) regressive line first order : y = 0.04852018354126351 * x + -3.6092085257707875
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for adaptation PE - break : -0.4457325098173891
standard deviation for differences in Skin Conductance for adaptation PE - break : 0.5988067805546274
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8821191787719727, pvalue=1.8070706573780626e-05)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.003276154121549, pvalue=0.0451476555340976)
__________________________________________________
Skew : -1.2466230898160848
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-5.908236565764221, pvalue=1.519548448533127e-07) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=226.0, pvalue=5.220594547594895e-08) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Skin Conductance : pearson r = 0.19775457367563284 (p = 0.11726235058991033) regressive line first order : y = 0.008525632725628356 * x + -0.7681652788500858
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Skin Conductance : pearson r = -0.023918494944029634 (p = 0.8511875302545213) regressive line first order : y = -0.0013666764284053768 * x + -0.40648327113912164
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Skin Conductance : pearson r = 0.12622620451583985 (p = 0.32027664245695897) regressive line first order : y = 0.003245173533478997 * x + -0.510838803832811
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for adaptation PE - break : -3.3722686611209163
standard deviation for differences in Phasic Component for adaptation PE - break : 6.1654532804507856
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5300037860870361, pvalue=5.276682411327338e-13)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.544015651991301, pvalue=5.5192556911887885e-06)
__________________________________________________
Skew : -2.9253866760922436
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-4.341376290878497, pvalue=5.234863462308522e-05) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=3.5254980732707397e-12) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Phasic Component : pearson r = 0.17605821688588302 (p = 0.16404264971596172) regressive line first order : y = 0.07815102598840325 * x + -6.327879408513269
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Phasic Component : pearson r = 0.03054829198560656 (p = 0.810620538190978) regressive line first order : y = 0.01797202815730106 * x + -3.888402844763403
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Phasic Component : pearson r = 0.038603443207981133 (p = 0.7620013511364361) regressive line first order : y = 0.01021863169753519 * x + -3.5772799595527136
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for adaptation PE - break : 2.529901908202659
standard deviation for differences in Tonic Component for adaptation PE - break : 6.576278383327561
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5779573321342468, pvalue=2.784567715297115e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.446720432402023, pvalue=8.719119124066543e-06)
__________________________________________________
Skew : 2.8265866546748195
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for adaptation PE - break : Ttest results : Ttest_relResult(statistic=3.0534707779988124, pvalue=0.0033119345614391845) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=706.0, pvalue=0.02550765413881958) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Tonic Component : pearson r = -0.16857334039577707 (p = 0.18300519307679514) regressive line first order : y = -0.07981462099524148 * x + 5.548428519070034
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Tonic Component : pearson r = -0.03569359936950389 (p = 0.779469771543885) regressive line first order : y = -0.02239833212558111 * x + 3.1731540089341883
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Tonic Component : pearson r = -0.005258833219721763 (p = 0.967103166319851) regressive line first order : y = -0.0014848113518934338 * x + 2.559690935950019
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for adaptation PE - break : -18.64199508846803
standard deviation for differences in HR[1] for adaptation PE - break : 20.914472043316376
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8059670925140381, pvalue=9.49068379441087e-08)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.394080455712731, pvalue=1.112426143075688e-05)
__________________________________________________
Skew : 0.7490733508982098
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-7.074825916335157, pvalue=1.479944738655821e-09) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=79.0, pvalue=1.3041066034311836e-10) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Heart Rate(HR)[threshold : 0%] : pearson r = 0.1940646625404734 (p = 0.12439938344895488) regressive line first order : y = 0.29221784995821015 * x + -29.693445878692238
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Heart Rate(HR)[threshold : 0%] : pearson r = 0.04667010482545472 (p = 0.7142144647802925) regressive line first order : y = 0.09313884079517726 * x + -21.316826172554507
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Heart Rate(HR)[threshold : 0%] : pearson r = -0.17555985972497198 (p = 0.16525845769708883) regressive line first order : y = -0.15764268504530604 * x + -15.479288719746563
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for adaptation PE - break : -18.051345700773588
standard deviation for differences in HR[0.9] for adaptation PE - break : 13.950344004737254
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8552100658416748, pvalue=8.128967238008045e-06)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=3.1037410060015005, pvalue=0.0019109052878476303)
__________________________________________________
Skew : -1.5936335613155985
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-9.596348493675679, pvalue=2.390419005929095e-13) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=12.0, pvalue=1.4412466780473427e-10) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Heart Rate(HR)[threshold : 9%] : pearson r = 0.19139637243660962 (p = 0.15763311830251267) regressive line first order : y = 0.19393988946820248 * x + -25.247242874629407
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Heart Rate(HR)[threshold : 9%] : pearson r = 0.2406271207899987 (p = 0.07403049249443815) regressive line first order : y = 0.3183900107236746 * x + -27.347196906723724
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Heart Rate(HR)[threshold : 9%] : pearson r = -0.13328973492884216 (p = 0.32741648678272806) regressive line first order : y = -0.07991379945904087 * x + -16.39313436199849
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for adaptation PE - break : -13.214311749548658
standard deviation for differences in HR[0.5] for adaptation PE - break : 6.41601948912551
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9245086908340454, pvalue=0.019269609823822975)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=1.5165846752901542, pvalue=0.12937157692956971)
__________________________________________________
Skew : -0.9891786297814428
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-12.009317656485063, pvalue=8.80843968474649e-14) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=2.4770276393652366e-07) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.15893669508356525 (p = 0.3617954705761054) regressive line first order : y = 0.06273383592395337 * x + -15.624617421559433
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.2877655507594347 (p = 0.09368252702535507) regressive line first order : y = 0.15977643578923229 * x + -18.295202407646247
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.21775099010222815 (p = 0.20890193965103612) regressive line first order : y = 0.059661995905182945 * x + -14.393914640016849
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for adaptation PE - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for adaptation PE - break : -12.088191428215913
standard deviation for differences in HR[0.1] for adaptation PE - break : 3.230967532703601
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.980011522769928, pvalue=0.9346803426742554)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=-0.4778987691652218, pvalue=0.6327222574825806)
__________________________________________________
Skew : 0.1027277462327408
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for adaptation PE - break : Ttest results : Ttest_relResult(statistic=-7.482706839892499, pvalue=0.0017056808099062436) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=0.0625) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - break in Heart Rate(HR)[threshold : 90%] : pearson r = 0.7294550190056841 (p = 0.16189445089414872) regressive line first order : y = 0.11943746413778324 * x + -18.260241824999994
____________________________________________________________________________________________________ correlation for Age and adaptation PE - break in Heart Rate(HR)[threshold : 90%] : pearson r = 0.7268554319996954 (p = 0.16416309196985962) regressive line first order : y = 0.18258301469442592 * x + -17.090966030843187
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - break in Heart Rate(HR)[threshold : 90%] : pearson r = 0.6550826511383132 (p = 0.23017108224584912) regressive line first order : y = 0.06244191836726519 * x + -13.449425248622294
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for adaptation PE - adaptation VE : -0.25290591424191866
standard deviation for differences in Skin Conductance for adaptation PE - adaptation VE : 0.3871170050801228
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.963276743888855, pvalue=0.05395703390240669)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.1495573618316897, pvalue=0.031590244162566374)
__________________________________________________
Skew : -0.6533623649964706
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=-5.1854566867429694, pvalue=2.4300226837938967e-06) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=338.0, pvalue=2.6709193274650893e-06) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Skin Conductance : pearson r = -0.0361522227975298 (p = 0.7767090867055705) regressive line first order : y = -0.0010076057280800738 * x + -0.21479905279790512
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Skin Conductance : pearson r = 0.09895485315192819 (p = 0.43659942235688265) regressive line first order : y = 0.0036553123520700983 * x + -0.35788191585293155
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Skin Conductance : pearson r = 0.09979742663960654 (p = 0.43268484460308765) regressive line first order : y = 0.0016586825827092943 * x + -0.2861832335575237
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for adaptation PE - adaptation VE : -0.11594100306745878
standard deviation for differences in Phasic Component for adaptation PE - adaptation VE : 0.625816080668854
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.567838728427887, pvalue=1.939559407179714e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.59121504769811, pvalue=2.2548610341279905e-08)
__________________________________________________
Skew : -1.6107607894578506
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=-1.4704850371247709, pvalue=0.1464074945010088) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=640.0, pvalue=0.007472883093784643) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Phasic Component : pearson r = 0.16529091308558658 (p = 0.1918028571677723) regressive line first order : y = 0.007447475288923172 * x + -0.3975987001544634
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Phasic Component : pearson r = -0.05423699789038881 (p = 0.6703572226830937) regressive line first order : y = -0.0032388251808685774 * x + -0.022925992404389268
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Phasic Component : pearson r = -0.09007226441970748 (p = 0.4790590346023314) regressive line first order : y = -0.0024201344386394685 * x + -0.06738705589225441
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for adaptation PE - adaptation VE : -0.5472503234449438
standard deviation for differences in Tonic Component for adaptation PE - adaptation VE : 1.2015799560317284
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.969699501991272, pvalue=0.11668615788221359)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.093588634139755, pvalue=0.03629664091359675)
__________________________________________________
Skew : -0.42771982571161743
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=-3.614961085527834, pvalue=0.000597862935199117) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=538.0, pvalue=0.0007875682553027494) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Tonic Component : pearson r = -0.26126975664280666 (p = 0.037039012645387365) regressive line first order : y = -0.02260243295049247 * x + 0.30755603259193875
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Tonic Component : pearson r = 0.14394423575981716 (p = 0.2564678099306155) regressive line first order : y = 0.01650410961893723 * x + -1.0212277215637968
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Tonic Component : pearson r = 0.06468996414144243 (p = 0.6115569377069762) regressive line first order : y = 0.0033372694880866877 * x + -0.6142042925496826
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for adaptation PE - adaptation VE : -2.4365443237287394
standard deviation for differences in HR[1] for adaptation PE - adaptation VE : 23.003712016968468
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5553779602050781, pvalue=1.2525511227171049e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.4380003514130495, pvalue=5.388186837656961e-08)
__________________________________________________
Skew : 0.5549217050422672
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=-0.8407108819068557, pvalue=0.40368949505406004) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=944.0, pvalue=0.5208728899052881) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = 0.15061181144345323 (p = 0.23485800903063644) regressive line first order : y = 0.24944242945118578 * x + -11.87026212867457
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = 0.001177598584542229 (p = 0.9926315515895575) regressive line first order : y = 0.0025848797433599842 * x + -2.5107788388583567
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.21168890955866504 (p = 0.09310859244512269) regressive line first order : y = -0.20907288886542505 * x + 1.7579805091338514
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for adaptation PE - adaptation VE : -1.1150511488653765
standard deviation for differences in HR[0.9] for adaptation PE - adaptation VE : 6.929790648940066
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8503413796424866, pvalue=5.881916877115145e-06)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=3.4409835097330403, pvalue=0.0005796038554688752)
__________________________________________________
Skew : -1.5510472187092326
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=-1.1933175274947914, pvalue=0.23786721923893522) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=738.0, pvalue=0.624540793570172) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = 0.13609682240354537 (p = 0.3172389108431163) regressive line first order : y = 0.06850410937361749 * x + -3.6568104970367363
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = 0.11741258778842378 (p = 0.3887950467751543) regressive line first order : y = 0.07717288154657968 * x + -3.3682236725914074
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = -0.11226789153048888 (p = 0.41005535520509934) regressive line first order : y = -0.03343611721736765 * x + -0.42125171660499716
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for adaptation PE - adaptation VE : 0.29988461129600075
standard deviation for differences in HR[0.5] for adaptation PE - adaptation VE : 3.4630094515060867
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9844468832015991, pvalue=0.890385091304779)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.5371692863068526, pvalue=0.5911506846816894)
__________________________________________________
Skew : 0.26008609309383074
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=0.5049402165826831, pvalue=0.6168582276487) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=298.0, pvalue=0.780670449239729) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.0798276385493368 (p = 0.6485022325055363) regressive line first order : y = -0.01700665690347314 * x + 0.9532998057067572
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = 0.04926633486859512 (p = 0.7786727957735421) regressive line first order : y = 0.01476427696852377 * x + -0.16961939630305495
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.015817900417883432 (p = 0.928138697590123) regressive line first order : y = -0.002339238370371656 * x + 0.3461346956473491
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for adaptation PE - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for adaptation PE - adaptation VE : 1.104808569566336
standard deviation for differences in HR[0.1] for adaptation PE - adaptation VE : 3.4018700006524143
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9635338187217712, pvalue=0.8323512077331543)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.7404104939061542, pvalue=0.45905095325918344)
__________________________________________________
Skew : 0.3550743676202116
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for adaptation PE - adaptation VE : Ttest results : Ttest_relResult(statistic=0.6495301521542297, pvalue=0.5514092408726096) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=5.0, pvalue=0.625) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = 0.4513625834261687 (p = 0.44546540567041565) regressive line first order : y = 0.07781310246179056 * x + -2.916261313249153
____________________________________________________________________________________________________ correlation for Age and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = 0.03494051002081941 (p = 0.955521414629247) regressive line first order : y = 0.009241164322147838 * x + 0.8516006671394856
____________________________________________________________________________________________________ correlation for MSSQ and adaptation PE - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.017169525564716598 (p = 0.9781401552070448) regressive line first order : y = -0.0017231518611926927 * x + 1.1423732801403368
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for instruction - break : -0.40414574488935395
standard deviation for differences in Skin Conductance for instruction - break : 0.5360395926921621
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8234680891036987, pvalue=2.7998075324831007e-07)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=3.8245208402144644, pvalue=0.00013102666315449672)
__________________________________________________
Skew : -1.923677258222796
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for instruction - break : Ttest results : Ttest_relResult(statistic=-5.984273264398095, pvalue=1.128904761940322e-07) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=140.0, pvalue=1.7576449327996724e-09) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Skin Conductance : pearson r = 0.10496707094480781 (p = 0.4091048626949124) regressive line first order : y = 0.004051010052285478 * x + -0.5573517802151869
____________________________________________________________________________________________________ correlation for Age and instruction - break in Skin Conductance : pearson r = -0.0010526908856824321 (p = 0.993413103417778) regressive line first order : y = -5.3844689568450106e-05 * x + -0.4025993927108097
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Skin Conductance : pearson r = 0.08090004790753993 (p = 0.5251105114489395) regressive line first order : y = 0.0018618613629713327 * x + -0.44149933848396605
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for instruction - break : -2.3131436219466335
standard deviation for differences in Phasic Component for instruction - break : 5.18889058527904
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5529558658599854, pvalue=1.1516412823375788e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.036104573905845, pvalue=4.751010715433824e-07)
__________________________________________________
Skew : -3.2584888749295473
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for instruction - break : Ttest results : Ttest_relResult(statistic=-3.538330209433519, pvalue=0.0007624544221168367) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=200.0, pvalue=1.9373574696720898e-08) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Phasic Component : pearson r = 0.10813174507214608 (p = 0.3950459850638382) regressive line first order : y = 0.04039625441412207 * x + -3.8408984043149674
____________________________________________________________________________________________________ correlation for Age and instruction - break in Phasic Component : pearson r = 0.07543436478313917 (p = 0.5535653683438613) regressive line first order : y = 0.037349852998329755 * x + -3.3857847127424145
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Phasic Component : pearson r = -0.009175914172853558 (p = 0.942632148317975) regressive line first order : y = -0.002044210186753647 * x + -2.2721316550748876
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for instruction - break : 1.4821612215027704
standard deviation for differences in Tonic Component for instruction - break : 5.601800573609638
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5783933997154236, pvalue=2.8286932258342334e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.943369770894517, pvalue=7.67836483475594e-07)
__________________________________________________
Skew : 3.173512911878682
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for instruction - break : Ttest results : Ttest_relResult(statistic=2.100090824443539, pvalue=0.03973076449934634) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=1011.0, pvalue=0.8462247135727818) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Tonic Component : pearson r = -0.1290462635440692 (p = 0.3094960685427498) regressive line first order : y = -0.05204589147047654 * x + 3.45049617606348
____________________________________________________________________________________________________ correlation for Age and instruction - break in Tonic Component : pearson r = -0.07483196534126091 (p = 0.5567459975643289) regressive line first order : y = -0.039999995390283545 * x + 2.6309110891174745
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Tonic Component : pearson r = 0.01957894056408199 (p = 0.8779573749258435) regressive line first order : y = 0.0047088897678885236 * x + 1.3876891205345065
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for instruction - break : -14.968774038501737
standard deviation for differences in HR[1] for instruction - break : 17.984629792583238
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8593689799308777, pvalue=3.201282197551336e-06)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.731680270362165, pvalue=0.006301225823510125)
__________________________________________________
Skew : -0.6178015882858184
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for instruction - break : Ttest results : Ttest_relResult(statistic=-6.60625000249845, pvalue=9.656036140112103e-09) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=123.0, pvalue=8.654036635008001e-10) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Heart Rate(HR)[threshold : 0%] : pearson r = 0.09221172060061006 (p = 0.46863574395300606) regressive line first order : y = 0.11939908589080062 * x + -19.48435418635595
____________________________________________________________________________________________________ correlation for Age and instruction - break in Heart Rate(HR)[threshold : 0%] : pearson r = 0.02466954279818764 (p = 0.8465705296356542) regressive line first order : y = 0.04233579790599054 * x + -16.18460523461439
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Heart Rate(HR)[threshold : 0%] : pearson r = -0.2319534447136433 (p = 0.06514005305820197) regressive line first order : y = -0.17910347875839785 * x + -11.375510495911373
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for instruction - break : -14.71455689216395
standard deviation for differences in HR[0.9] for instruction - break : 12.59928535818303
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8213378190994263, pvalue=9.57415068114642e-07)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=3.1752781236165846, pvalue=0.001496929502456302)
__________________________________________________
Skew : -1.8006258429749622
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for instruction - break : Ttest results : Ttest_relResult(statistic=-8.661290816075827, pvalue=7.390310765676266e-12) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=3.0, pvalue=8.880118337733133e-11) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Heart Rate(HR)[threshold : 9%] : pearson r = 0.1083923480154099 (p = 0.4265051236076558) regressive line first order : y = 0.09919574895431138 * x + -18.395091162427487
____________________________________________________________________________________________________ correlation for Age and instruction - break in Heart Rate(HR)[threshold : 9%] : pearson r = 0.24783974911993092 (p = 0.06552183359672469) regressive line first order : y = 0.296173921021958 * x + -23.361777622001473
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Heart Rate(HR)[threshold : 9%] : pearson r = -0.2223433758247007 (p = 0.09955258306805641) regressive line first order : y = -0.12039550302033838 * x + -12.216350204491935
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for instruction - break : -10.05218769555063
standard deviation for differences in HR[0.5] for instruction - break : 6.1096656520340975
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9744642376899719, pvalue=0.5767477750778198)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.32965276675347893, pvalue=0.7416623470569804)
__________________________________________________
Skew : -0.4102588035848532
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for instruction - break : Ttest results : Ttest_relResult(statistic=-9.593622012883344, pvalue=3.338578877137246e-11) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=3.0, pvalue=3.21660235864676e-07) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.0195628421710153 (p = 0.9111860425041008) regressive line first order : y = 0.007352946171776947 * x + -10.334696290837357
____________________________________________________________________________________________________ correlation for Age and instruction - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.22475370082391466 (p = 0.19426934379638847) regressive line first order : y = 0.11883176517287398 * x + -13.831037828048029
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Heart Rate(HR)[threshold : 50%] : pearson r = 0.024376760098561174 (p = 0.8894518574965075) regressive line first order : y = 0.006360121214528381 * x + -10.17793637784931
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for instruction - break _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for instruction - break : -9.53672290364467
standard deviation for differences in HR[0.1] for instruction - break : 3.6233332372614484
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8950603008270264, pvalue=0.38318881392478943)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.41024448808797165, pvalue=0.6816266090403336)
__________________________________________________
Skew : 0.7856190341524252
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for instruction - break : Ttest results : Ttest_relResult(statistic=-5.264060619968048, pvalue=0.006237255803204973) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=0.0625) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - break in Heart Rate(HR)[threshold : 90%] : pearson r = -0.18335134990635685 (p = 0.767864497498319) regressive line first order : y = -0.03366680107530871 * x + -7.796957291277017
____________________________________________________________________________________________________ correlation for Age and instruction - break in Heart Rate(HR)[threshold : 90%] : pearson r = 0.2845458428853254 (p = 0.6426550945252999) regressive line first order : y = 0.08015677991413198 * x + -11.733018673291891
____________________________________________________________________________________________________ correlation for MSSQ and instruction - break in Heart Rate(HR)[threshold : 90%] : pearson r = 0.1302379447537478 (p = 0.8346458811042021) regressive line first order : y = 0.01392173482600168 * x + -9.84021672285151
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for instruction - adaptation VE : -0.21131914931388346
standard deviation for differences in Skin Conductance for instruction - adaptation VE : 0.3643904832279706
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9540634155273438, pvalue=0.018221784383058548)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=1.5576741378995747, pvalue=0.11931051418163126)
__________________________________________________
Skew : -0.7926019908207348
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=-4.603011950785312, pvalue=2.0700622492450034e-05) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=417.0, pvalue=3.095623039605578e-05) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Skin Conductance : pearson r = -0.20896652730649212 (p = 0.09749232737241775) regressive line first order : y = -0.005482228401422949 * x + -0.003985554163006124
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Skin Conductance : pearson r = 0.14288347733082568 (p = 0.26002605969373366) regressive line first order : y = 0.004968144090907025 * x + -0.3539980374246194
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Skin Conductance : pearson r = 0.017601454461666714 (p = 0.8902032715414826) regressive line first order : y = 0.0002753704122016278 * x + -0.21684376820867848
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for instruction - adaptation VE : 0.9431840361068242
standard deviation for differences in Phasic Component for instruction - adaptation VE : 2.4923945138059085
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.44935137033462524, pvalue=4.141976881920015e-14)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.635651902352348, pvalue=1.7439733948080454e-08)
__________________________________________________
Skew : 4.1233048209851875
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=3.0036541802857823, pvalue=0.0038235529074255145) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=195.0, pvalue=1.5957142030065998e-08) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Phasic Component : pearson r = -0.1688950939129351 (p = 0.18215878835806035) regressive line first order : y = -0.030307296285357988 * x + 2.0893823040438395
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Phasic Component : pearson r = 0.06786012880827275 (p = 0.5941747356711409) regressive line first order : y = 0.016138999660160126 * x + 0.4796921396166003
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Phasic Component : pearson r = -0.1372131136655733 (p = 0.2796109323660665) regressive line first order : y = -0.014682976322928285 * x + 1.2377612485855725
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for instruction - adaptation VE : -1.5949910101448315
standard deviation for differences in Tonic Component for instruction - adaptation VE : 2.4518956763160484
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5671716928482056, pvalue=1.8942753182010774e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=5.732440153006553, pvalue=9.899596980085408e-09)
__________________________________________________
Skew : -4.015532215344958
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=-5.163290098745998, pvalue=2.640929922791321e-06) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=80.0, pvalue=1.362703043012955e-10) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Tonic Component : pearson r = 0.02926604068051169 (p = 0.8184313746239368) regressive line first order : y = 0.005166296574272427 * x + -1.7903763104146153
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Tonic Component : pearson r = -0.0046911424706981425 (p = 0.9706527203250558) regressive line first order : y = -0.0010975536457651636 * x + -1.5634706413805124
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Tonic Component : pearson r = 0.09053853233642986 (p = 0.47677694022933664) regressive line first order : y = 0.009530970607868654 * x + -1.7862061079651956
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for instruction - adaptation VE : 1.2366767262375506
standard deviation for differences in HR[1] for instruction - adaptation VE : 20.54475290004109
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.7100059390068054, pvalue=6.068925917368517e-10)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.7659693216883685, pvalue=1.8794785774867397e-06)
__________________________________________________
Skew : -1.267607737897151
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=0.4777773311354142, pvalue=0.6344622958755326) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=692.0, pvalue=0.0199518762104551) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = 0.051802250444090786 (p = 0.6843581447055661) regressive line first order : y = 0.07662366538377605 * x + -1.661170436338277
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.024596006192000256 (p = 0.8470223652085702) regressive line first order : y = -0.048218163145826706 * x + 2.62144209908176
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.2613555778909223 (p = 0.03697467251767135) regressive line first order : y = -0.2305336825785169 * x + 5.861758732969044
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for instruction - adaptation VE : 2.221737659744261
standard deviation for differences in HR[0.9] for instruction - adaptation VE : 7.161175288216645
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9794214963912964, pvalue=0.4513145089149475)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=1.3379143488999006, pvalue=0.1809243590995755)
__________________________________________________
Skew : -0.08261438191811776
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=2.300858002740231, pvalue=0.02521538825713963) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=494.0, pvalue=0.013147317535407698) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = -0.05044655544345725 (p = 0.7119576032773096) regressive line first order : y = -0.02624003114027355 * x + 3.1953412151651865
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = 0.08091091754869223 (p = 0.5533157879848903) regressive line first order : y = 0.05495679184486299 * x + 0.6171956121308503
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = -0.2401732817089109 (p = 0.07459392531929972) regressive line first order : y = -0.0739178207786651 * x + 3.755532440901563
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for instruction - adaptation VE : 3.462008665294026
standard deviation for differences in HR[0.5] for instruction - adaptation VE : 6.212039295202644
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.9628006219863892, pvalue=0.2770136594772339)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=1.8462124806633415, pvalue=0.06486135948005661)
__________________________________________________
Skew : 0.695324230428211
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=3.249626254369747, pvalue=0.00260540822093728) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=134.0, pvalue=0.00303052215866995) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.189416441074576 (p = 0.27579750961722743) regressive line first order : y = -0.07238754665564967 * x + 6.243220936428836
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.04870053427103645 (p = 0.7811524989086359) regressive line first order : y = -0.026180393647834452 * x + 4.294545183295162
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.20974405008135513 (p = 0.22654563631893504) regressive line first order : y = -0.05564111306102616 * x + 4.562112957814885
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for instruction - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for instruction - adaptation VE : 3.656277094137579
standard deviation for differences in HR[0.1] for instruction - adaptation VE : 3.429569135982622
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.7966683506965637, pvalue=0.0761021226644516)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.24044300380442118, pvalue=0.8099868436016422)
__________________________________________________
Skew : 0.8483124503702244
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for instruction - adaptation VE : Ttest results : Ttest_relResult(statistic=2.1322078367083286, pvalue=0.09995940895638043) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=0.0625) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and instruction - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.43320651437673263 (p = 0.46619804241778295) regressive line first order : y = -0.07529116275130147 * x + 7.547023220473834
____________________________________________________________________________________________________ correlation for Age and instruction - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.34948378975266253 (p = 0.5642552329731696) regressive line first order : y = -0.09318507045814604 * x + 6.209548024690783
____________________________________________________________________________________________________ correlation for MSSQ and instruction - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.49658243630726695 (p = 0.394774902087647) regressive line first order : y = -0.05024333540245621 * x + 4.751581805911126
_ _ _ _ _ _ _ _ _ _ _ _ Test of Skin Conductance for : break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Skin Conductance for break - adaptation VE : 0.19282659557547036
standard deviation for differences in Skin Conductance for break - adaptation VE : 0.5133051934311436
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.78011155128479, pvalue=2.1304321862203324e-08)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.438558985586015, pvalue=9.056316645032197e-06)
__________________________________________________
Skew : 2.3017707600203092
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Skin Conductance for break - adaptation VE : Ttest results : Ttest_relResult(statistic=2.981683555401383, pvalue=0.004071783573931597) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=697.0, pvalue=0.021801178429516347) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Skin Conductance : pearson r = -0.2579594391670416 (p = 0.03959266122139927) regressive line first order : y = -0.009533238453708428 * x + 0.5533662260521808
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Skin Conductance : pearson r = 0.10253093874165958 (p = 0.4201223490676054) regressive line first order : y = 0.005021988780475475 * x + 0.04860135528619019
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Skin Conductance : pearson r = -0.07198802331480617 (p = 0.5718780160644552) regressive line first order : y = -0.001586490950769704 * x + 0.22465557027528738
_ _ _ _ _ _ _ _ _ _ _ _ Test of Phasic Component for : break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Phasic Component for break - adaptation VE : 3.2563276580534573
standard deviation for differences in Phasic Component for break - adaptation VE : 5.976564003679676
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.523467481136322, pvalue=4.245969081970863e-13)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.3807231299776035, pvalue=1.1828609464351587e-05)
__________________________________________________
Skew : 2.841939894962698
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Phasic Component for break - adaptation VE : Ttest results : Ttest_relResult(statistic=4.324608503437646, pvalue=5.550959324501533e-05) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=3.5254980732707397e-12) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Phasic Component : pearson r = -0.16431464614908511 (p = 0.19447707754375823) regressive line first order : y = -0.07070355069948006 * x + 5.930280708358807
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Phasic Component : pearson r = -0.03719301798574838 (p = 0.7704542646563473) regressive line first order : y = -0.021210853338169647 * x + 3.8654768523590146
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Phasic Component : pearson r = -0.049255123324741454 (p = 0.6991193669357942) regressive line first order : y = -0.012638766136174632 * x + 3.5098929036604596
_ _ _ _ _ _ _ _ _ _ _ _ Test of Tonic Component for : break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in Tonic Component for break - adaptation VE : -3.077152231647603
standard deviation for differences in Tonic Component for break - adaptation VE : 6.337228035247639
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.5624464750289917, pvalue=1.603458685273118e-12)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.4542323580636545, pvalue=8.419388186623977e-06)
__________________________________________________
Skew : -2.8696175119841616
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of Tonic Component for break - adaptation VE : Ttest results : Ttest_relResult(statistic=-3.854072871898242, pvalue=0.00027499341373340584) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=253.0, pvalue=1.416725365381946e-07) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Tonic Component : pearson r = 0.12539373797675427 (p = 0.323504242484653) regressive line first order : y = 0.05721218804474897 * x + -5.240872486478096
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Tonic Component : pearson r = 0.06433278906243327 (p = 0.6135290442075828) regressive line first order : y = 0.038902441744518346 * x + -4.1943817304979865
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Tonic Component : pearson r = 0.017722845835507428 (p = 0.8894507696318197) regressive line first order : y = 0.004822080839980141 * x + -3.1738952284997013
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 0%] for break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 0%] for break - adaptation VE : 16.20545076473929
standard deviation for differences in HR[1] for break - adaptation VE : 17.28466248834081
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.7679628729820251, pvalue=1.0965713670429977e-08)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=4.866630057417685, pvalue=1.1351723086025046e-06)
__________________________________________________
Skew : -1.3577566312203637
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 0% for break - adaptation VE : Ttest results : Ttest_relResult(statistic=7.4416713608593055, pvalue=3.383334483006864e-10) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=67.0, pvalue=7.669596820068871e-11) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.034373203578604385 (p = 0.7874329517735762) regressive line first order : y = -0.0427754205070244 * x + 17.823183750017673
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.05490367339022965 (p = 0.6665426828162897) regressive line first order : y = -0.09055396105181729 * x + 18.806047333696153
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Heart Rate(HR)[threshold : 0%] : pearson r = -0.06930357681760722 (p = 0.5863339608386696) regressive line first order : y = -0.05143020382011904 * x + 17.237269228880418
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 9%] for break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 9%] for break - adaptation VE : 16.936294551908215
standard deviation for differences in HR[0.9] for break - adaptation VE : 12.089059078988381
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8302947282791138, pvalue=1.6453141142847016e-06)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=2.351456509048589, pvalue=0.018700075542518235)
__________________________________________________
Skew : 1.5219306867879703
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 9% for break - adaptation VE : Ttest results : Ttest_relResult(statistic=10.389801324668413, pvalue=1.3932452681948422e-14) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=1.0, pvalue=7.968377335678e-11) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = -0.1428500546008978 (p = 0.2935896048569698) regressive line first order : y = -0.12543578009458498 * x + 21.590432377592673
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = -0.2103709182285414 (p = 0.11965170402504591) regressive line first order : y = -0.24121712917709498 * x + 23.978973234132322
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Heart Rate(HR)[threshold : 9%] : pearson r = 0.08945647982918839 (p = 0.5120500881134453) regressive line first order : y = 0.04647768224167327 * x + 15.9718826453935
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 50%] for break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 50%] for break - adaptation VE : 13.514196360844663
standard deviation for differences in HR[0.5] for break - adaptation VE : 7.026927509503556
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.8471341133117676, pvalue=0.00019866309594362974)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=3.047296309460059, pvalue=0.0023090996461239176)
__________________________________________________
Skew : 1.6985646477401755
__________________________________________________
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 50% for break - adaptation VE : Ttest results : Ttest_relResult(statistic=11.214094463192463, pvalue=5.729799706111197e-13) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=2.4770276393652366e-07) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.18445968002773555 (p = 0.28878894941031413) regressive line first order : y = -0.07974049282742665 * x + 16.577917227266187
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.23846832010351093 (p = 0.16772263330949969) regressive line first order : y = -0.14501215882070845 * x + 18.12558301134319
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Heart Rate(HR)[threshold : 50%] : pearson r = -0.20661549915788968 (p = 0.23370713321849493) regressive line first order : y = -0.06200123427555463 * x + 14.74004933566419
_ _ _ _ _ _ _ _ _ _ _ _ Test of Heart Rate(HR)[threshold : 90%] for break - adaptation VE _ _ _ _ _ _ _ _ _ _ _ _
____________________________________________________________________________________________________
________________________________________________________________________________
mean for differences in HR[threshold : 90%] for break - adaptation VE : 13.192999997782248
standard deviation for differences in HR[0.1] for break - adaptation VE : 2.6235560422735142
________________________________________________________________________________
Shapiro-Wilk :
ShapiroResult(statistic=0.964346706867218, pvalue=0.837878406047821)
__________________________________________________
kurtosistest :
KurtosistestResult(statistic=0.6320258672085752, pvalue=0.5273699746727198)
__________________________________________________
Skew : -0.1693690227313645
__________________________________________________
C:\Users\Krzyniu\anaconda3\lib\site-packages\scipy\stats\stats.py:1603: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=5
warnings.warn("kurtosistest only valid for n>=20 ... continuing "
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>> test results of threshold : 90% for break - adaptation VE : Ttest results : Ttest_relResult(statistic=10.057341856017295, pvalue=0.0005496970393904364) .................................................................................................... Wilcoxon signed-rank test results : WilcoxonResult(statistic=0.0, pvalue=0.0625) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ____________________________________________________________________________________________________ correlation for Break length[s] and break - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.313074559020595 (p = 0.6079921678838585) regressive line first order : y = -0.04162436167599273 * x + 15.343980511750846
____________________________________________________________________________________________________ correlation for Age and break - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.8498325147195402 (p = 0.06825980466034755) regressive line first order : y = -0.173341850372278 * x + 17.94256669798267
____________________________________________________________________________________________________ correlation for MSSQ and break - adaptation VE in Heart Rate(HR)[threshold : 90%] : pearson r = -0.8290119349326204 (p = 0.08266424482103452) regressive line first order : y = -0.0641650702284579 * x + 14.591798528762633
statisticalTestsOnData in 4.669492299959529s
| N | mean difference | standard deviation | Shapiro-Wilk statistic | Shapiro-Wilk pvalue | kurtosis statistic | kurtosis pvalue | skew | Ttest statistic | Ttest pvalue | Wilcoxon statistic | Wilcoxon pvalue | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| adaptation PE - instruction | threshold : 0% | 64 | -3.673 | 8.922 | 0.792 | 0.000 | 4.811 | 0.000 | 1.068 | -3.268 | 0.002 | 329.000 | 0.000 |
| threshold : 9% | 56 | -3.337 | 5.433 | 0.936 | 0.005 | 2.454 | 0.014 | 0.264 | -4.555 | 0.000 | 252.000 | 0.000 | |
| threshold : 50% | 35 | -3.162 | 4.574 | 0.940 | 0.057 | 0.883 | 0.377 | -0.836 | -4.031 | 0.000 | 103.000 | 0.001 | |
| threshold : 90% | 5 | -2.551 | 3.759 | 0.918 | 0.515 | -0.519 | 0.604 | -0.496 | -1.357 | 0.246 | 4.000 | 0.438 | |
| adaptation PE - break | threshold : 0% | 64 | -18.642 | 20.914 | 0.806 | 0.000 | 4.394 | 0.000 | 0.749 | -7.075 | 0.000 | 79.000 | 0.000 |
| threshold : 9% | 56 | -18.051 | 13.950 | 0.855 | 0.000 | 3.104 | 0.002 | -1.594 | -9.596 | 0.000 | 12.000 | 0.000 | |
| threshold : 50% | 35 | -13.214 | 6.416 | 0.925 | 0.019 | 1.517 | 0.129 | -0.989 | -12.009 | 0.000 | 0.000 | 0.000 | |
| threshold : 90% | 5 | -12.088 | 3.231 | 0.980 | 0.935 | -0.478 | 0.633 | 0.103 | -7.483 | 0.002 | 0.000 | 0.062 | |
| adaptation PE - adaptation VE | threshold : 0% | 64 | -2.437 | 23.004 | 0.555 | 0.000 | 5.438 | 0.000 | 0.555 | -0.841 | 0.404 | 944.000 | 0.521 |
| threshold : 9% | 56 | -1.115 | 6.930 | 0.850 | 0.000 | 3.441 | 0.001 | -1.551 | -1.193 | 0.238 | 738.000 | 0.625 | |
| threshold : 50% | 35 | 0.300 | 3.463 | 0.984 | 0.890 | 0.537 | 0.591 | 0.260 | 0.505 | 0.617 | 298.000 | 0.781 | |
| threshold : 90% | 5 | 1.105 | 3.402 | 0.964 | 0.832 | 0.740 | 0.459 | 0.355 | 0.650 | 0.551 | 5.000 | 0.625 | |
| instruction - break | threshold : 0% | 64 | -14.969 | 17.985 | 0.859 | 0.000 | 2.732 | 0.006 | -0.618 | -6.606 | 0.000 | 123.000 | 0.000 |
| threshold : 9% | 56 | -14.715 | 12.599 | 0.821 | 0.000 | 3.175 | 0.001 | -1.801 | -8.661 | 0.000 | 3.000 | 0.000 | |
| threshold : 50% | 35 | -10.052 | 6.110 | 0.974 | 0.577 | 0.330 | 0.742 | -0.410 | -9.594 | 0.000 | 3.000 | 0.000 | |
| threshold : 90% | 5 | -9.537 | 3.623 | 0.895 | 0.383 | 0.410 | 0.682 | 0.786 | -5.264 | 0.006 | 0.000 | 0.062 | |
| instruction - adaptation VE | threshold : 0% | 64 | 1.237 | 20.545 | 0.710 | 0.000 | 4.766 | 0.000 | -1.268 | 0.478 | 0.634 | 692.000 | 0.020 |
| threshold : 9% | 56 | 2.222 | 7.161 | 0.979 | 0.451 | 1.338 | 0.181 | -0.083 | 2.301 | 0.025 | 494.000 | 0.013 | |
| threshold : 50% | 35 | 3.462 | 6.212 | 0.963 | 0.277 | 1.846 | 0.065 | 0.695 | 3.250 | 0.003 | 134.000 | 0.003 | |
| threshold : 90% | 5 | 3.656 | 3.430 | 0.797 | 0.076 | 0.240 | 0.810 | 0.848 | 2.132 | 0.100 | 0.000 | 0.062 | |
| break - adaptation VE | threshold : 0% | 64 | 16.205 | 17.285 | 0.768 | 0.000 | 4.867 | 0.000 | -1.358 | 7.442 | 0.000 | 67.000 | 0.000 |
| threshold : 9% | 56 | 16.936 | 12.089 | 0.830 | 0.000 | 2.351 | 0.019 | 1.522 | 10.390 | 0.000 | 1.000 | 0.000 | |
| threshold : 50% | 35 | 13.514 | 7.027 | 0.847 | 0.000 | 3.047 | 0.002 | 1.699 | 11.214 | 0.000 | 0.000 | 0.000 | |
| threshold : 90% | 5 | 13.193 | 2.624 | 0.964 | 0.838 | 0.632 | 0.527 | -0.169 | 10.057 | 0.001 | 0.000 | 0.062 | |
| adaptation PE - instruction | Skin Conductance | 64 | -0.042 | 0.276 | 0.940 | 0.004 | 2.855 | 0.004 | -1.024 | -1.195 | 0.237 | 943.000 | 0.517 |
| Phasic Component | 64 | -1.059 | 2.567 | 0.399 | 0.000 | 5.790 | 0.000 | -4.410 | -3.274 | 0.002 | 78.000 | 0.000 | |
| Tonic Component | 64 | 1.048 | 2.603 | 0.477 | 0.000 | 5.827 | 0.000 | 4.311 | 3.195 | 0.002 | 267.000 | 0.000 | |
| adaptation PE - break | Skin Conductance | 64 | -0.446 | 0.599 | 0.882 | 0.000 | 2.003 | 0.045 | -1.247 | -5.908 | 0.000 | 226.000 | 0.000 |
| Phasic Component | 64 | -3.372 | 6.165 | 0.530 | 0.000 | 4.544 | 0.000 | -2.925 | -4.341 | 0.000 | 0.000 | 0.000 | |
| Tonic Component | 64 | 2.530 | 6.576 | 0.578 | 0.000 | 4.447 | 0.000 | 2.827 | 3.053 | 0.003 | 706.000 | 0.026 | |
| adaptation PE - adaptation VE | Skin Conductance | 64 | -0.253 | 0.387 | 0.963 | 0.054 | 2.150 | 0.032 | -0.653 | -5.185 | 0.000 | 338.000 | 0.000 |
| Phasic Component | 64 | -0.116 | 0.626 | 0.568 | 0.000 | 5.591 | 0.000 | -1.611 | -1.470 | 0.146 | 640.000 | 0.007 | |
| Tonic Component | 64 | -0.547 | 1.202 | 0.970 | 0.117 | 2.094 | 0.036 | -0.428 | -3.615 | 0.001 | 538.000 | 0.001 | |
| instruction - break | Skin Conductance | 64 | -0.404 | 0.536 | 0.823 | 0.000 | 3.825 | 0.000 | -1.924 | -5.984 | 0.000 | 140.000 | 0.000 |
| Phasic Component | 64 | -2.313 | 5.189 | 0.553 | 0.000 | 5.036 | 0.000 | -3.258 | -3.538 | 0.001 | 200.000 | 0.000 | |
| Tonic Component | 64 | 1.482 | 5.602 | 0.578 | 0.000 | 4.943 | 0.000 | 3.174 | 2.100 | 0.040 | 1011.000 | 0.846 | |
| instruction - adaptation VE | Skin Conductance | 64 | -0.211 | 0.364 | 0.954 | 0.018 | 1.558 | 0.119 | -0.793 | -4.603 | 0.000 | 417.000 | 0.000 |
| Phasic Component | 64 | 0.943 | 2.492 | 0.449 | 0.000 | 5.636 | 0.000 | 4.123 | 3.004 | 0.004 | 195.000 | 0.000 | |
| Tonic Component | 64 | -1.595 | 2.452 | 0.567 | 0.000 | 5.732 | 0.000 | -4.016 | -5.163 | 0.000 | 80.000 | 0.000 | |
| break - adaptation VE | Skin Conductance | 64 | 0.193 | 0.513 | 0.780 | 0.000 | 4.439 | 0.000 | 2.302 | 2.982 | 0.004 | 697.000 | 0.022 |
| Phasic Component | 64 | 3.256 | 5.977 | 0.523 | 0.000 | 4.381 | 0.000 | 2.842 | 4.325 | 0.000 | 0.000 | 0.000 | |
| Tonic Component | 64 | -3.077 | 6.337 | 0.562 | 0.000 | 4.454 | 0.000 | -2.870 | -3.854 | 0.000 | 253.000 | 0.000 |
main in 14.078966100001708s